Prioritizing the Effective Factors on Knowledge Commercialization Using Fuzzy Analytic Hierarchy Process: A Case Study.
Biranvand, Ali ; Seif, Hassan
Prioritizing the Effective Factors on Knowledge Commercialization Using Fuzzy Analytic Hierarchy Process: A Case Study.
Introduction
Economic evolutions and the reduction of public research funding
have led to academic reforms in many countries to increase the
commercialization of research results. In recent decades, universities
played a more important role in the invention and innovation process.
And along it, new perspectives on the role of the university in
knowledge production system, the old mission of universities meaning
education and research, have changed gradually, and have undertaken a
"third mission" titling commercial activities, including
inventions, licensing and company establishment (Baldini, 2006a). At
present, the universities are expected not only to support and maintain
the economic growth, but also to play a role in creation of economic
growth through production of new knowledge, human capital, licensing
innovation, and creating new companies.
Formation of expectations about the direct participation of
universities in economic growth, allowing universities to grant patents,
and establishing technology transfer offices are examples of reforming
the process of knowledge commercialization. (Etzkowitz, Webster,
Gebhardt, Terra, 2000) have explained academic revolution as the
acceptance of the commercialization function of knowledge as one of the
main tasks of universities. In fact, the academic revolution led the
universities to knowledge commercialization, opportunism, and the nature
of entrepreneurship, and caused the emergence and growth of
fourth-generation universities according to the knowledge-based economy.
Since higher education is one of the main infrastructures of the
development of each country, paying attention to higher education is
always one of the important concerns of decision-makers and planners in
each society. Because paying attention to the commercialization of
research results is one of the manifestations of accepting the
importance and position of science and technology and its effect on the
economic, social and cultural development of societies.
Nowadays, the intervention of universities into knowledge
commercialization has become a necessity from being an advantage. This
will be achieved when there is an effective relation and communication
between the university and industry. Considering the infrastructures and
common opinions in every society (universities and research centers),
recognition of effective factors on the knowledge commercialization as
well as prioritizing these factors requires conducting research with the
priority of localization of commercialization patterns. Therefore, the
present study tends to introduce the prioritization of the effective
factors on the knowledge commercialization using the fuzzy hierarchy
process by examining the opinions of experts in the knowledge
commercialization field at Isfahan University.
Literature Review
Commercializing the academic knowledge includes the economic usage
of intellectual properties (Rasmussen, Moen, [??] Gulbrandsen, 2006).
For the first time, the academic knowledge commercialization entered the
Economic Development Program of United States in the 1980s and then it
was expanded into European countries in the 1990s.
The American model of knowledge commercialization is based on an
entrepreneurial university model that contributes in technology transfer
through a patent and establishing a new company (Etzkowitz, 2003).
Bayh-Dole Act has been a turning point in creation of knowledge
commercialization in the United States. After the approval of the
Bayh-Dole Act, the US universities increased their efforts in technology
transfer, issuing licenses, and investment in new companies. After
twenty years, the number of universities that contributed to licensing
for technology was eight times more, and the volume of registered
inventions of universities increased four times (Mowery [??] Shane,
2002)
However, Bayh-Dole Act was not the only factor of increasing the
commercialization of academic research (Kortum [??] Lerner, 1999;
Mowery, Nelson, Sampat, [??] Ziedonis, 2001). The Increase in amount and
complexity of scientific researches, the increase of the demand of
society for scientific research; increased competition among scientists,
and use of business activities including registering patents and new
companies as criteria for ranking universities were effective in
creating this conversion in academic culture (Kumar, 2010). As a result
of Bayh-Dole Act and the above factors, helping to the economic growth
has become the "third mission" of US universities, and
includes commercial activities such as registering patents, licensing,
and establishment of companies, along with education and research
(Baldini, 2006b).
The knowledge commercialization in European countries is known as
the "third mission" of universities (Van Geenhuizen, 2010).
This relatively new role began to emerge in Europe since the early
1980s. The first knowledge commercialization began in the United Kingdom
in the early Lockett, 2002), and then was expanded to a region in
Netherlands, and [??] 1980s (Wright, Vohora, Europe countries, and
recently is expanded to Southern European countries, then to other
Northern including France and Italy. In the 1990s, the actions related
to technology commercialization expanded in many European countries.
Effective Factors on Knowledge Commercialization
According to the investigated community, many factors can influence
the knowledge commercialization. Some of the identified factors in
previous researches include policy formulation (Heidari & Pourezzat,
2011), supplying operating institutes (Fakour, Hosseini, 2008;
Hmieleski, Powell, 2018), providing financial resources (Fakour,
Hosseini, 2008),(Heidari & Pourezzat, 2011),(Hmieleski, Powell,
2018) expansion of contribution between university, industry, and
government (Mozaffari & Shamsi, 2011), (Hashemnia, Emadzadeh,
Samadi, Saketi, 2009), (Heidari, Pourezzat, 2011), the number and rank
of faculty members, supporting the dissertations of higher education
students, Networking (Abbasi Esfanjani, Foruzandeh Dehkordi, 2015;
Gholipour, Pourezzat, 2011; Kalantari, Poori, Yadollahi Farsi, 2015;
Yadollahi Farsi, Zarea, Hejazi, 2012), human resource management (Abbasi
Esfanjani [??] Foruzandeh Dehkordi, 2015; Zare [??] Mirjalili, 2014),
the formation of commercial companies (Abbasi Esfanjani, Foruzandeh
Dehkordi, 2015; Jalili, Mousakhani, Behboudi, 2011; Mozaffari [??]
Shamsi, 2011; Wu, ++) creativity and innovation (Jahed [??] Arasteh,
2013), Creation and expansion of commercialization culture (Fakour,
Hosseini, 2008; Hafezi, Ekrami, Ghorchiyan, Sarmadi, 2016; Hmieleski,
Powell, 2018; Kalantari, MigoonPoori, Farsi, 2015).
The Analytic Hierarchy Process
The analytic hierarchy process is the decision-making process of
choosing a strategy among the existing strategies or prioritizing the
proposed strategies. One of the novel methods of decision-making is
Multiple Criteria Decision Making (MCDM). One of the first methods of
decision-making with multiple criteria is Analytical Hierarchy Process
(AHP), which is used more than other methods in management science. The
Analytical Hierarchy Process can be used in case of decision-making
practice encountering multiple competing choices and decision criteria.
The proposed criteria can be quantitative or qualitative. The base of
this decision-making method is the pairwise comparison. The
decision-maker begins by providing a hierarchical tree. The hierarch
decision tree represents the evaluated compared factors and competing
choices in the decision. Then a series of pairwise comparisons are
performed. These comparisons indicate the weight of each of the factors
in line with the evaluated competing choices in the decision.
Ultimately, the logic of the analysis hierarchy process combines the
matrixes derived from the pairwise comparison in a way to obtain the
optimal decision.
In the ultimate stage, using the analysis hierarchy process, the
main, sub-criteria, and final criteria that affect the knowledge
commercialization will be prioritized.
Objectives and Research Questions
This article as part of an expanded research that identifies the
effective factors on the knowledge commercialization at Isfahan
University, has focused on prioritizing the identified and effective
criteria on knowledge commercialization at Isfahan University.
The main question of the present study is that how is the
prioritization of the effective factors on knowledge commercialization
at Isfahan University? In this regard, the effective factors on
knowledge commercialization at Isfahan University are already identified
and in this study the criteria, sub-criteria and identified indexes are
prioritized. It is obvious that prioritizing the mentioned factors will
affect the decision making by authorities and researchers to promote the
commercialization of research results of the university.
Methodology
Regarding that the results of this research will be applied in
developing the applied knowledge in the field of commercialization of
the academic research results and the presentation of a native model, in
this regard, it is applicable. Likewise, regarding the approach aspect,
this research applies mixed research method. The research community
consists of 790 individuals including policy makers (managers),
executives (staff of the entrepreneurship and industrial relationships
department), researchers and faculty members of Isfahan University.
Regarding the heterogeneity of the community in this section, 260
individuals were selected using stratified sampling. A questionnaire is
the data collection tool is in this research. In order to calculate the
reliability of the questionnaire the Cronbach alpha coefficient was
used. Accordingly, the Cronbach alpha coefficient for all aspects was
calculated greater than 0.7 and for the total questionnaire was 0.895.
Therefore, the reliability of the questionnaire was evaluated to be
desirable.
The Research Process
After identifying the effective indexes on knowledge
commercialization through studying the previous resources, these indexes
were introduced to Delphi panel members in three stages. The result of
performing Delphi for three times was identification of 29 effective
indexes (10 contextual indexes, 9 structural indexes, and 10 content
indexes) (Table 2). Then, the identified indicators were classified in
the form of sub-criteria related to the main criteria (contextual
criteria include four sub-criteria of "innovative
infrastructure", "political and legal environment",
"technical, economic and market environment", and
"Commercialization culture". The structural criteria include
three sub-criteria of "financial and informational resources",
"strategic links" and "hard abilities, processes, and
capabilities". Content criteria include three sub-criteria of
"knowledge base and research quality", "soft
capabilities, human skills and marketing", and "internal
management of the organization". After placing the identified
indexes in the related sub-criteria, the criteria, sub-criteria, and
identified indexes, were prioritized. AHP was used to prioritize the
criteria.
Prioritizing the criteria, sub-criteria and ultimate indexes
affecting knowledge commercialization at Isfahan University.
The fuzzy analytic hierarchy process is used to determine the
priority of criteria, sub-criteria and effective indexes on knowledge
commercialization. The analytic process is as follows:
1. Pairwise comparison of main criteria according to the purpose
and determining the weight of main criteria;
2. Pairwise comparison of the sub criteria of each criteria and
determining the weight of the sub criteria of each cluster;
3. Determine the weight of the ultimate indexes of each
sub-criteria;
4. Calculate the final weight of the criteria, sub-criteria and
final ultimate indexes. Nine-point scale is used for pairwise comparison
of the elements. Likewise, the fuzzy approach is used to quantify the
values in this study.
Determining the priority of the main criteria according to the
purpose
In order to perform the analytic hierarchy process, first the main
criteria were pairwise compared according to the purpose.
The results of normalization of criteria are presented in Table 4.
Accordingly, the priority Eigen vector of the main criteria will be
as ([W.sub.1]).
[mathematical expression not reproducible]
According to the obtained Eigen vector:
--The contextual factor with a normal weight of 0.465 has the
highest priority.
--The content factor with a normal weight of 0.321 is in the
middle.
--The last one is the structural factor with a normal weight of
0.214.
The inconsistency rate of the performed comparisons is 0.073, which
is less than 0.1 and therefore the comparisons are reliable.
Determining the priority of the sub criteria of the investigated
factors
In the second step of the fuzzy AHP the sub-criteria related to
each of the main criteria are compared pairwise. The pairwise comparison
of each cluster is investigated separately.
--Pairwise comparison of the structural sub-criteria
Structural sub-criteria include "financial and informational
resources", "strategic links", "networking" and
"hard capabilities, processes, technology, capabilities".
Fuzzy value of the mean of the experts' opinion is presented in
Table 6 to determine the priority of the structural sub-criteria.
The results of the Defuzzification of the structural sub-criteria
are as follows:
According to the obtained normal weight:
--The S1 index with a weight of 0.359 has the highest priority.
--The S3 index with a weight of 0.343 is in the second priority.
--And the last one is the S2 index with a weight of 0.298.
The inconsistency rate of the comparisons is also 0.001 and is at
the tolerance threshold of 0.1.
--A pairwise comparison of content subcategories
Content sub-criteria include: knowledge base and research quality,
soft capabilities: human skills and marketing, internal management of
the organization. Fuzzy values of the mean of experts' opinion are
calculated to determine the priority of content sub-criteria.
The Defuzzification results of the content sub-criteria are as
follows:
Based on the obtained normal weight:
--The S4 index with a weight of 0.507 has the highest priority.
--The S5 index with a weight of 0.301 has the second priority.
--And the last one is the S6 index with a weight of 0.192.
The inconsistency rate of the performed comparisons is also 0.078
and is at the tolerance threshold of 0.1.
--Pairwise comparison of the contextual sub-criteria
Contextual sub-criteria include "Innovative infrastructure in
the national information system", "political and legal
environment", "Technical, economic, and market
environment", and "Commercialization Culture". Fuzzy
values of the mean of experts' opinion is presented in Table 10 to
determine the priority of sub-criteria. Since four indexes are used,
therefore six pairwise comparisons are performed.
The Defuzzification results of the contextual sub-criteria are as
follows:
According to the obtained normal weight:
--The S10 index with a weight of 0.396 has the first priority.
--The S7 index with a weight of 0.214 is in the second priority.
--The S9 index with a weight of 0.209 is in the third priority.
--And the last one is the S2 index with a weight of 0.181.
The inconsistency rate of the performed comparisons is 0.043 and is
at the tolerance threshold of 0.1.
Determining the priority of the ultimate criteria
Since the present study is a four-level hierarchical research,
therefore in the third step of the Fuzzy AHP, the ultimate indexes
related to each of the sub-criteria is compared pairwise.
--Pairwise comparison of the financial and informational indexes
The financial and informational indexes include "providence of
the required financial resources" and "access to the
informational resources". Fuzzy values of the mean of the
experts' opinion are presented in Table 12 to determine the
priority of the indexes of the financial and informational resources.
The SS2 index with a weight of 0.577 is the most important one.
Since only one comparison is performed, so there is no need to calculate
the inconsistency rate.
--Pairwise comparison of strategic links
Indexes of the strategic links include "creating strategic
relations between university and industry", and "communication
between the researchers, inventors and executives of the business
plans". The fuzzy values of the mean of the experts' opinion
is presented in Table 13 to determine the priority of strategic link
indexes. SS4 index with a weight of 0.564 is the most important. Since
only one comparison is performed, so there is no need to calculate the
inconsistency rate.
--Pairwise comparison of the indexes of the hard abilities
The indexes of hard capabilities include, "strategic
programming of the researches", "the alignment of policies and
rules with commercialization purpose", "establishment of a
commercialization center/ institution", "documenting and
introducing successful experiences of commercialization",
"having lab, workshop and equipment". Fuzzy values of the mean
of experts' opinions are calculated to determine the priority of
the indexes of hard capabilities.
Therefore the SS7 index with a weight of 0.245 is in the top
priority. The SS9 index with a weight of 0.238 is in the second
priority. The SS6 index with a weight of 0.196 is in the third priority
and the SS8 index with a weight of 0.150 is the last one. The
inconsistency rate is obtained 0.019 and is less than tolerance
threshold of 0.1. So the results are reliable.
--Pairwise comparison of the knowledge base indexes
The knowledge base indexes are: "paying attention and focusing
on the needs of the market and customer", "management of
research and commercialization projects" and "processing the
results for different purposes". The fuzzy values of the mean of
experts' opinions is calculated to determine the priority of the
base knowledge indexes.
Therefore, the SS12 index with a weight of 0.491 is in the first
priority. The SS10 index with a weight of 0.336 is in the second
priority and the SS11 index with a weight of 0.173 in in the last
priority. The inconsistency rate is 0.064 and is less than tolerance
threshold of 0.1, so the results are reliable.
--Pairwise comparison of the indexes of soft capabilities
The indexes of soft capabilities include: "enriching the
universities with research base and position", "ability to
execute and operationalization of the research results",
"Users' knowledge and belief of the research results".
Fuzzy values of the mean of experts' opinions is calculated to
prioritize the indexes of soft capabilities.
Therefore, the SS13 index with a weight of 0.490 is in the first
priority. The SS14 index with a weight of 0.280 is in the second
priority and the SS15 index with a weight of 0.229 is in the last
priority. The inconsistency rate is obtained 0.008, so the results are
reliable.
--Pairwise comparison of internal management indexes
Internal management indexes include: "incentive supporting
system of commercialization", "Strengthening and promoting
commercialization culture in universities", "Training courses
of teachers and higher education students", "Training and
attracting people with commercialization skills". The fuzzy values
of the mean of the experts' opinions to prioritize the indexes of
internal management are presented in Table 17.
Therefore, the SS17 index with a weight of 0.307 is the first
priority. The SS16 index with a weight of 0.262 is in the second
priority. The SS19 index with a weight of 0.258 is in the third priority
and the SS18 index with a weight of 0.173 is the last one. The
inconsistency rate is obtained 0.054 and is less than 0.1 tolerance
threshold. So the results are reliable.
--Pairwise comparison of the indexes of innovative infrastructures
Performance indexes include: "creation and expansion of
communication circles between institutions and related
organizations", "infrastructures of communication",
"comprehensive information network of research results". The
fuzzy values of the mean of experts' opinions are calculated to
determine the priority of performance indexes.
Therefore, the SS21 index with a weight of 0.429 is in the top
priority. The SS20 index with a weight of 0.422 is in the second
priority and the SS22 index is with a weight of 0.150 is the last one.
The inconsistency rate is obtained 0.098, so the results are reliable.
--Pairwise comparison of the indexes of political and legal
environment
Indexes of the political and legal environment include:
"supportive policies", "rules and regulations of
supporting the commercialization". The fuzzy values of the mean of
experts' opinions are calculated to determine the priority of the
indexes of political and legal environment.
Therefore, the SS24 index with a weight of 0.548 has the highest
importance. Since one pairwise comparison is performed, there is no need
to calculate the compatibility.
--Pairwise comparison of the indexes of technical, economic and
market environment
The indexes of the technical, economic, and market environment
include: "expanding the science and technology parks, development
centers and national laboratories", "market demand and demand
for research results", "capacity of receiving and transferring
research results", "risk taking capacity, and venture
capitalist". The fuzzy values of the mean of the experts'
opinions are calculated to prioritize the performance indexes.
Therefore, the SS26 index with a weight of 0.327 is in the top
priority. The SS28 index with a weight of 0.312 is in the second
priority. The SS25 index with a weight of 0.224 is in the third priority
and the SS27 index with a weight of 0.138 is the last one. The
inconsistency rate is 0.028, so the results are reliable.
The ultimate priority of the effective indexes on knowledge
commercialization using fuzzy AHP technique
In order to determine the final priority of the factors using the
fuzzy AHP technique, the weights related to the main criteria
([W.sub.1]) and the weight of the indexes based on each criteria should
be obtained ([W.sub.2]). The comparison results of the sub-criteria of
the research and their weights form the ([W.sub.2]) matrix. In order to
determine the ultimate priority of the indexes with AHP technique, it is
enough to multiply the weight of the indexes based on each criterion
([W.sub.2]) to the weight of the main criteria ([W.sub.1]). Each of
these matrixes are calculated in previous steps. The calculation results
out and the weights related to the indexes are given in Table 21:
According to the data of Table 21 and Figure 10, based on the
obtained weight by calculating with the fuzzy technique, the contextual
criteria among the main criteria, and the sub criteria of
commercialization culture, the knowledge base and the research quality
among the sub-criteria, respectively, are in the first and second
priorities. The prioritization of the indexes indicated that the
expansion and promotion of the commercialization and entrepreneurial,
processing the results for different purposes and the focusing and
paying attention to the needs of market and customer are respectively in
the first to third priorities.
Conclusion
In the first step, Defuzzification of the ultimate weights of the
main criteria indicates that the contextual factors with a weight of
0.456 are the first priority, content factors with a weight of 0.339 are
in the second priority, and structural factors with a weight of 0.226
are in the third priority of the effective factors on the
commercialization of knowledge.
In the second step, using the fuzzy AHP technique, the sub-criteria
related to each of the main factors were compared pairwise. The results
of Defuzzification of structural sub-criteria indicates that the
"financial and informational resources" sub-criteria with a
weight of 0.359 is of prime importance. The "hard capabilities,
processes, technology, and capacities" sub-criteria with a weight
of 0.343 is in the second priority, and the sub-criteria of
"strategic links, networking" with a weight of 0.298 is in the
third priority. The results of Defuzzification of content sub-criteria
show that the "knowledge base and research quality"
sub-criteria with a weight of 0.507 is of the first priority. The
"soft capabilities; human skills and marketing" sub-criteria
with a weight of 0.301 is in the second priority and the "internal
management of the organization" sub-criteria with a weight of 0.192
is in the third priority. The Defuzzification results of contextual
sub-criteria indicate that the "commercialization culture"
sub-criteria with a weight of 0.396 is the first priority. The
"innovative infrastructure in the national information system"
sub-criteria with a weight of 0.214 is in the second priority, the
"technical, economic and market environment" sub-criteria with
a weight of 0.209 is in the third priority, and the "political and
legal environment" sub-criteria with a weight of 0.181 is in the
fourth priority.
In the third step, the pairwise comparison of the indexes in each
sub-criteria determines the ultimate priority of the effective indexes
on knowledge commercialization at the Isfahan University. Results
indicate that:
Among the indexes related to the sub-criteria of financial and
informational resources, the "providence of the required financial
resources" with a weight of 0.577 is the most important one. The
"access to the informational resources" index, with a weight
of 0.423, is in the second priority. Among the indexes of the strategic
links, the "establishing strategic relations between university and
industry" index with a weight of 0.564 is in the first priority and
the index of "interaction between researchers, inventors and
managers of business plans" with a weight of 0.436 is in the second
priority.
Among the five indexes related to hard capabilities sub-criteria,
the "creating a commercialization center/institution" index
with a weight of 0.245 is in the first priority, the "having
laboratory" index is in the second priority, the "alignment of
policies and rules with commercialization purpose" index is in the
third priority, "strategic programming of the researches"
index is the fourth priority and the "documentation and
introduction of successful commercialization experiences" is in the
fifth priority.
Pairwise comparison of the indexes of knowledge base indicate that
the "processing the results for different purposes" index with
a weight of 0.491 is in the first priority, the "focusing and
paying attention to market and customer needs" index is in the
second priority and the index of "management of the research and
commercialization projects" is in the third priority of importance.
The fuzzy values of the experts' opinions to prioritize the
soft capabilities index indicates that the "enriching the
universities with research base and position" with a weight of
0.490 is in the first priority. The "ability to execute and
operationalization of research results" and "users'
knowledge and belief of the research results" are in the second and
third priorities.
Among the four indexes of internal management, the index of
"developing and promoting commercialization culture in
universities" with a weight of 0.307 is in the first priority.
Other indicators of the "incentive supporting system of
commercialization", "training courses of teachers and higher
education students", and "Training and attracting people with
commercialization skills" are in the second to fourth priority.
Pairwise comparison of the sub-criteria of innovative
infrastructures indicate that the index of "infrastructures of
communication" with a weight of 0.429 is in the first priority, and
the indexes of "creating and expansion of communication circles
between institutions and related organizations" and
"comprehensive information network of research results" are in
the second and third priority.
The Defuzzification values related to the sub-criteria of the
political and legal environment confirm that the index of "rules
and regulations supporting the commercialization" with the weight
of 0.548 is in the first priority and the index of "supportive
policies" is in the second priority.
Among the four indexes of the "technical, economic and market
environment" sub-criteria, the "market demand and demand for
research results" index is in the first priority and other indexes
of "risk taking capability, venture capitalist",
"expanding the science and technology parks, development centers
and national laboratories", and "capacity of receiving and
transferring research results" are respectively, in the next
priorities.
In the fourth step, the ultimate weight of the criteria, the
sub-criteria and the ultimate indexes are calculated. The results of
this part of the fuzzy AHP indicate that the index of "expansion
and promotion of commercialization and entrepreneurship culture" is
in the first priority of the effect of commercialization of knowledge.
The result is that in order to commercialize knowledge at Isfahan
University, the indexes of "expansion and promotion
commercialization and entrepreneurship culture", "processing
the results for different purposes" and "focusing and paying
attention to the needs of the market and customer" should be the
priorities for the authorities, respectively. The expansion of
commercialization culture leads to carry out researches with the goal of
producing capital and profitability and prevention of research without
the purpose of capital production. Paying attention to the processing of
research results for different purposes makes it possible to avoid
single-dimensionality and single-product production. Because
single-dimensionality in some cases may lead to failure and waste of
costs. The next priority is paying attention to the needs of the market
and customer. Paying attention to the need of the market prevents the
researches and production of products that do not have any customer, and
directs the cost and human resources to conduct researches that is
required by the customer.
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Ali Biranvand, Assistant Professor, Department of Knowledge and
Information Science, Payame Noor University, Iran. biranvand@gmail.com
Hassan Seif Associate Professor, Department of Educational
Sciences, Payame Noor University, Iran. hassanseif@gmail.com
Table 1. Computing of Cronbach alpha coefficient.
Commercialization Contextual
steps indexes
Number of questions 9 10
Cronbach alpha coefficient 0.783 0.770
Structural Content Total
indexes indexes questionnaire
Number of questions 9 10 38
Cronbach alpha coefficient 0.738 0.873 0.895
Table 2. Prioritizing the criteria of the sub-criteria and final
indicators
Criteria Sub-criteria Symbol Ultimate Criteria Symbol
Structural Financial and S1 Providence of the SS01
informational required financial
resources resources
Access to the SS02
informational
resources
Strategic S2 Creating strategic SS03
links, relations between
networking university and
industry
Communication SS04
between the
researchers,
inventors and
executives of the
business plans
Hard abilities, S3 Strategic SS05
processes, and programming of the
capabilities researches
The alignment of SS06
policies and rules
with
commercialization
purpose
Establishment of a SS07
commercialization
center/
institution
Documenting and SS08
introducing
successful
experiences of
commercialization
Having lab, SS09
workshop and
equipment
Content Knowledge base S4 Paying attention SS10
and research and focusing on
quality the needs of the
market and
customer
Management of SS11
research and
commercialization
projects
Processing the SS12
results for
different purposes
Soft S5 Enriching the SS13
capabilities; universities with
human skills research base and
and marketing position
Ability to execute SS14
and
operationalization
of the research
results
Users' knowledge SS15
and belief of the
research results
Internal S6 incentive SS16
management of supporting system
the of
organization commercialization
Strengthening and SS17
promoting
commercialization
culture in
universities
Training courses SS18
of teachers and
higher education
students
Training and SS19
attracting people
with
commercialization
skills
Contextual Innovative S7 Creation and SS20
infrastructures expansion of
in national communication
information circles between
system institutions and
related
organizations
Infrastructures of SS21
communication
Comprehensive SS22
information
network of
research results
Political and S8 Supportive SS23
legal policies
environment
Rules and SS24
regulations
supporting the
Commercialization
Technical, S9 Expanding the SS25
economic and science and
market technology parks,
environment development
centers and
national
laboratories
Market demand and SS26
demand for
research results
Capacity of SS27
receiving and
transferring
research results
Risk taking SS28
capacity, venture
capitalist
Commercialization S10 Expansion and SS29
culture promotion of
commercialization
and
entrepreneurship
culture
Table 3. The Fuzzy Scale Equal to Nine-point Scale in an AHP
Technique (Sarafrazi, Izadiyar & Habibi, 2014, p. 77)
Verbal phrase Fuzzy equivalent Reverse fuzzy
equivalent
Preferred Equally (1, 1, 1) (1, 1, 1)
Midway (1, 2, 3) (1/3, 1/2, 1)
Preferred moderately (2, 3, 4) (1/4, 1/3, 1/2)
Midway (3, 4, 5) (1/5, 1/4, 1/3)
Preferred Strongly (4, 5, 6) (1/6, 1/5, 1/4)
Midway (5, 6, 7) (1/7, 1/6, 1/5)
very strongly Preferred (6, 7, 8) (1/8, 1/7, 1/6)
Midway (7, 8, 9) (1/9, 1/8, 1/7)
Extremely Preferred (9, 9, 9) (1/9, 1/9, 1/9)
Table 4. The Pairwise Comparison Matrix of the Main Criteria.
C1 C2 C3
C1 (1, 1, 1) (0.45, 0.59, 0.83) (0.45, 0.55, 0.74)
C2 (1.2, 1.7, 2.23) (1, 1, 1) (0.4, 0.51, 0.76)
C3 (1.36, 1.83, 2.23) (1.32, 1.94, 2.52) (1, 1, 1)
Fuzzy expansion Normal
C1 (1.9, 2.14, 2.57) (0.15, 0.21, 0.31)
C2 (2.6, 3.21, 3.99) (0.21, 0.32, 0.49)
C3 (3.68, 4.77, 5.75) (0.3, 0.47, 0.7)
Table 5. Defuzzification of the Ultimate Weights of the Main
Criteria
Crisp X1max X2max X3max Deffuzy Deffuzy
Structural 0.226 0.223 0.219 0.226 0.226
Content 0.339 0.334 0.328 0.339 0.339
Contextual 0.491 0.486 0.481 0.491 0.491
Table 6. Determining the Priority of Structural Sub-criteria.
S1 S2 S3
S1 (1, 1, 1) (0.97, 1.23, 1.52) (0.77, 1.04, 1.41)
S2 (0.66, 0.81, 1.04) (1, 1, 1) (0.65, 0.91, 1.19)
S3 (0.71, 0.96, 1.3) (0.84, 1.1, 1.55) (1, 1, 1)
Fuzzy expansion Normal
S1 (2.74, 3.27, 3.93) (0.25, 0.36, 0.52)
S2 (2.3, 2.72, 3.22) (0.21, 0.3, 0.42)
S3 (2.55, 3.06, 3.85) (0.23, 0.34, 0.51)
Table 7. Fuzzy Values of Structural Sub-criteria.
Crisp X1max X2max X3max Deffuzy Normal
S1 0.376 0.372 0.369 0.376 0.359
S2 0.312 0.309 0.306 0.312 0.298
S3 0.359 0.354 0.349 0.359 0.343
Table 8. Determining the Priority of Content Sub-criteria
S4 S5 S6
S4 (1, 1, 1) (2.19, 2.7, 3.19) (1.27, 1.86, 2.55)
S5 (0.31, 0.37, 0.46) (1, 1, 1) (1.37, 1.95, 2.5)
S6 (0.39, 0.54, 0.79) (0.4, 0.51, 0.73) (1, 1, 1)
Fuzzy expansion Normal
S4 (4.46, 5.56, 6.74) (0.34, 0.51, 0.75)
S5 (2.69, 3.32, 3.96) (0.2, 0.3, 0.44)
S6 (1.79, 2.05, 2.52) (0.14, 0.19, 0.28)
Table 9. Fuzzy Values of Content Sub-criteria.
Crisp X1max X2max X3max Deffuzy Normal
S4 0.533 0.527 0.521 0.533 0.507
S5 0.317 0.314 0.310 0.317 0.301
S6 0.201 0.198 0.194 0.201 0.192
Table 10. Determining the Priority of Contextual Sub-criteria.
S7 S8 S9
S7 (1, 1, 1) (1.05, 1.41, 1.76) (0.55, 0.72, 0.97)
S8 (0.57, 0.71, 0.95) (1, 1, 1) (0.73, 0.98, 1.29)
S9 (1.03, 1.39, 1.82) (0.77, 1.02, 1.36) (1, 1,1)
S10 (1.01, 1.38, 1.89) (1.38, 1.82, 2.28) (2.31, 2.87, 3.67)
S10 Fuzzy expansion Normal
S7 (0.53, 0.72, 0.99) (3.13, 3.86, 4.72) (0.14, 0.22, 0.32)
S8 (0.44, 0.55, 0.72) (2.74, 3.23, 3.97) (0.12, 0.18, 0.27)
S9 (0.27, 0.35, 0.43) (3.08, 3.76, 4.62) (0.14, 0.21, 0.32)
S10 (1, 1, 1) (5.7, 7.07, 8.84) (0.26, 0.39, 0.6)
Table 11. Fuzzy values of contextual Sub-criteria.
Crisp X1max X2max X3max Deffuzy Normal
S7 0.226 0.223 0.221 0.226 0.214
S8 0.192 0.189 0.186 0.192 0.181
S9 0.221 0.218 0.216 0.221 0.209
S10 0.419 0.412 0.406 0.419 0.396
Table 12. Prioritizing of the financial and informational indexes.
SS1 SS2
SS1 (1, 1, 1) (0.95,0.73, 0.580)
SS2 (1.71, 1.39, 1.05) (1, 1, 1)
Fuzzy expansion Normal
SS1 (1.95, 1.72, 1.58) (0.54, 0.42, 0.34)
SS2 (2.71, 3.39, 2.05) (0.74, 0.58, 0.44)
Table 13. Prioritizing of the strategic links indexes.
SS1 SS2
SS1 (1,1,1) (1.57, 1.32, 1.04)
SS2 (0.96, 0.76, 0.64) (1,1,1)
Fuzzy expansion Normal
SS1 (2.57, 2.32, 2.04) (0.7, 0.57, 0.45)
SS2 (1.96, 1.76, 1.64) (0.53, 0.43, 0.36)
Table 14. Prioritizing the of hard capabilities indexes.
SS5 SS6 SS7
SS5 (1, 1, 1) (0.6, 0.74, 0.95) (0.49, 0.59, 0.73)
SS6 (1.06, 1.35, 1.67) (1, 1, 1) (1.02, 1.23, 1.5)
SS7 (1.38, 1.69, 2.04) (0.67, 0.81, 0.98) (1, 1, 1)
SS8 (0.4, 0.47, 0.58) (0.52, 0.62, 0.74) (0.26, 0.3, 0.36)
SS9 (1.34, 1.62, (1.49, 1.74, 2.02) (1.64, 2.08, 2.66)
SS8 SS9
SS5 (1.72, 2.13, 2.52) (0.5, 0.62, 0.75)
SS6 (1.35, 1.63, 1.93) (0.5, 0.58, 0.67)
SS7 (2.78, 3.29, 3.8) (0.38, 0.48, 0.61)
SS8 (1, 1, 1) (1.7, 2.03, 2.44)
SS9 (0.59, 0.49, 0.59) (1, 1, 1)
Fuzzy expansion Normal
SS5 (4.31, 5.08, 5.94) (0.12, 0.17, 0.23)
SS6 (4.92, 5.78, 6.77) (0.14, 0.2, 0.26)
SS7 (6.2, 7.27, 8.43) (0.18, 0.25, 0.33)
SS8 (3.88, 4.42, 5.12) (0.11, 0.15, 0.2)
SS9 (6.06, 6.93, 8.25) (0.18, 0.24, 0.32)
Table 15. Prioritizing of the base knowledge indexes.
SS6 SS7 SS8
SS10 (1, 1, 1) (1.62, 2.21, 2.84) (0.35, 0.46, 0.65)
SS11 (0.35, 0.45, 0.62) (1, 1, 1) (0.37, 0.45, 0.55)
SS12 (1.53, 2.17, 2.84) (1.83, 2.22, 2.69) (1, 1, 1)
Fuzzy expansion Normal
SS10 (2.97, 3.67, 4.5) (0.23, 0.33, 0.5)
SS11 (1.72, 1.9, 2.16) (0.13, 0.17, 0.24)
SS12 (4.37, 5.4, 6.53) (0.33, 0.49, 0.72)
Table 16. Prioritizing of the soft capabilities indexes.
SS6 SS7 SS8
SS13 (1, 1, 1) (1.53, 1.95, 2.5) (1.43, 1.95, 2.53)
SS14 (0.4, 0.51, 0.66) (1, 1, 1) (0.96, 1.32, 1.71)
SS15 (0.39, 0.51, 0.7) (0.59, 0.76, 1.05) (1, 1, 1)
Fuzzy expansion Normal
SS13 (3.96, 4.9, 6.03) (0.33, 0.49, 0.73)
SS14 (2.36, 2.83, 3.36) (0.19, 0.28, 0.41)
SS15 (1.98, 2.27, 2.74) (0.16, 0.23, 0.33)
Table 17. Prioritizing of the internal management indexes.
SS16 SS17 SS18
SS16 (1, 1, 1) (0.75, 0.94, 1.2) (1.37, 1.9, 2.45)
SS17 (0.83, 1.06, 1.34) (1, 1, 1) (1.05, 1.3, 1.61)
SS18 (0.41, 0.53, 0.73) (0.62, 0.77, 0.95) (1, 1, 1)
SS19 (1.08, 1.45, 1.98) (0.4, 0.5, 0.66) (1.1, 1.44, 1.89)
SS19 Fuzzy expansion Normal
SS16 (0.5, 0.69, 0.92) (3.62, 4.53, 5.57) (0.17, 0.26, 0.39)
SS17 (1.5, 1.99, 2.49) (4.39, 5.35, 6.44) (0.21, 0.31, 0.45)
SS18 (0.53, 0.69, 0.91) (2.56, 2.99, 3.59) (0.12, 0.17, 0.25)
SS19 (1, 1, 1) (3.58, 4.4, 5.54) (0.17, 0.25, 0.39)
Table 18. Prioritizing of the innovative infrastructure indexes.
SS20 SS21 SS22
SS20 (1, 1, 1) (0.42, 0.52, 0.67) (2.59, 3.45, 4.26)
SS21 (1.48, 1.93, 2.36) (1, 1, 1) (1.71, 2.11, 2.61)
SS22 (0.23, 0.29, 0.39) (0.38, 0.47, 0.58) (1, 1, 1)
Fuzzy expansion Normal
SS20 (4.02, 4.97, 5.93) (0.29, 0.42, 0.6)
SS21 (4.19, 5.03, 5.97) (0.3, 0.43, 0.61)
SS22 (1.62, 1.76, 1.97) (0.12, 0.15, 0.2)
Table 19. Prioritizing the indexes of political and legal environment.
SS23 SS24
SS23 (1, 1, 1) (0.65, 0.82, 1.06)
SS24 (0.95, 1.22, 1.53) (1, 1, 1)
Fuzzy expansion Normal
SS23 (1.65, 1.82, 2.06) (0.36, 0.45, 0.57)
SS24 (1.95, 2.22, 2.53) (0.42, 0.55, 0.7)
Table 20. Prioritizing the indexes of technical, economic and
market environment.
SS25 SS26 SS27
SS25 (1, 1, 1) (0.59, 0.75, 0.93) (1.28, 1.73, 2.27)
SS26 (1.08, 1.34, 1.69) (1, 1, 1) (1.6, 1.99, 2.39)
SS27 (0.44, 0.58, 0.78) (0.42, 0.5, 0.63) (1, 1, 1)
SS28 (1.26, 1.69, 2.2) (0.46, 0.59, 0.85) (1.69, 2.4, 3.05)
SS28 Fuzzy expansion Normal
SS25 (0.45, 0.59, 0.79) (3.33, 4.07, 4.99) (0.15, 0.22, 0.34)
SS26 (1.18, 1.69, 2.17) (4.86, 6.02, 7.24) (0.22, 0.33, 0.49)
SS27 (0.33, 0.42, 0.59) (2.19, 2.5, 3) (0.1, 0.14, 0.2)
SS28 (1, 1, 1) (4.41, 5.68, 7.09) (0.2, 0.31, 0.48)
Table 21. Determining the ultimate priority of criteria, Sub-
criteria, and indexes using Fuzzy AHP Technique.
Criteria W Sub-criteria W1 W2
Structural 0.214 Financial and 0.359 0.077
informational resources
Strategic links, 0.298 0.064
networking
Hard abilities, 0.434 0.073
processes, and
capabilities
Knowledge base and 0.507 0.163
research quality
Content 0.321 Soft capabilities; human 0.301 0.097
skills and marketing
Internal management of 0.192 0.061
the organization
Innovative 0.214 0.099
infrastructures in
national information
system
Contextual 0.546 Political and legal 0.181 0.084
environment
Technical, economic and 0.209 0.097
market environment
Commercialization 0.396 0.184
culture
Criteria W Ultimate Criteria W1 W2
Structural 0.214 Providence of the 0.423 0.0326
required financial
resources
Access to the 0.577 0/0444
informational resources
Creating strategic 0.564 0.0360
relations between
university and industry
Communication between the 0.436 0.0278
researchers, inventors
and executives of the
business plans
Strategic programming of 0.172 0.0126
the researches
The alignment of policies 0.196 0.0144
and rules with
commercialization purpose
Establishment of a 0.245 0.0180
commercialization center/
institution
Documenting and 0.154 0.0110
introducing successful
experiences of
commercialization
Having lab, workshop and 0.238 0.0175
equipment
Paying attention and 0.336 0.0547
focusing on the needs of
the market and customer
Management of research 0.173 0.0281
and commercialization
projects
Processing the results 0.491 0.0799
for different purposes
Content 0.321 Enriching the 0.490 0.0474
universities with
research base and
position
Ability to execute and 0.280 0.0271
operationalization of the
research results
Users' knowledge and 0.229 0.0221
belief of the research
results
incentive supporting 0.262 0.0161
system of
commercialization
Strengthening and 0.307 0.0189
promoting
commercialization culture
in universities
Training courses of 0.173 0.0106
teachers and higher
education students
Training and attracting 0.258 0.0158
people with
commercialization skills
Creation and expansion of 0.422 0.0419
communication circles
between institutions and
related organizations
Infrastructures of 0.429 0.0426
communication
Comprehensive information 0.150 0.0149
network of research
results
Contextual 0.546 Supportive policies 0.452 0.0380
Rules and regulations 0.548 0.0462
supporting the
Commercialization
Expanding the science and 0.224 0.0218
technology parks,
development centers and
national laboratories
Market demand and demand 0.327 0.0318
for research results
Capacity of receiving and 0.138 0.0134
transferring research
results
Risk taking capacity, 0.423 0.0326
venture capitalist
Expansion and promotion 0.577 0.0444
of commercialization and
entrepreneurship culture
Fig 1. Fuzzy values of the financial and informational indexes.
SS1 0.423
SS2 0.577
Note: Table made from bar graph.
Fig 2. Fuzzy values of the strategic links indexes.
SS4 0.564
SS5 0.436
Note: Table made from bar graph.
Fig 3. Fuzzy values of the hard capabilities indexes.
SS5 0.172
SS6 0.196
SS7 0.245
SS8 0.150
SS9 0.238
Note: Table made from bar graph.
Fig 4. Fuzzy values of the base knowledge indexes.
SS10 0.336
SS11 0.173
SS12 0.491
Note: Table made from bar graph.
Fig 5. Fuzzy values of the soft capabilities indexes.
SS13 0.490
SS14 0.280
SS15 0.229
Note: Table made from bar graph.
Fig 6. Fuzzy values of the internal management indexes.
SS16 0.262
SS17 0.307
SS18 0.173
SS19 0.258
Note: Table made from bar graph.
Fig 7. Fuzzy values of the innovative infrastructure indexes.
SS20 0.422
SS21 0.429
SS22 0.150
Note: Table made from bar graph.
Fig 8. Fuzzy values of the innovative infrastructure indexes.
SS23 0.452
SS24 0.548
Note: Table made from bar graph.
Fig 9. Fuzzy values of the technical, economic and market
environment indexes.
SS25 0.224
SS26 0.327
SS27 0.138
SS28 0.312
Note: Table made from bar graph.
Figure 10. Output of the Fuzzy AHP Technic.
SS29 0.1839
SS28 0.0303
SS27 0.0134
SS26 0.0318
SS25 0.0218
SS24 0.0462
SS23 0.0380
SS22 0.0149
SS21 0.0426
SS20 0.0419
SS19 0.0158
SS18 0.0106
SS17 0.0189
SS16 0.0161
SS15 0.0221
SS14 0.0271
SS13 0.0474
SS12 0.0799
SS11 0.0281
SS10 0.0547
SS09 0.0175
SS08 0.0110
SS07 0.0180
SS06 0.0144
SS05 0.0126
SS04 0.0278
SS03 0.0360
SS02 0.0444
SS01 0.0326
Note: Table made from bar graph.
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