Performance evaluation of private universities based on balanced scorecard: empirical study based on Iran.
Zolfani, Sarfaraz Hashemkhani ; Ghadikolaei, Abdolhamid Safaei
1. Introduction
The concept of Balanced Scorecard (BSC) was proposed by David
Norton, the CEO of Nolan Norton Institute, and Robert Kaplan, a
professor at Harvard University (Kaplan, Norton 1992). The BSC is a
popular tool that is applied by many businesses to assess their
performance in diverse aspects of their organization (Frigo et al.
2000). Davis and Albright (2004), the Balanced Scorecard (BSC) is a
multi-attribute evaluation model that highlights the value of
non-financial attributes. Kaplan and Norton (1996) presented four
perspectives for performance measurement: financial, customer, internal
business process and learning and development perspectives. By combining
the financial, customer, internal process, and learning/growth
perspectives, the Balanced Scorecard helps managers to understand many
interrelationships and causal effects. This understanding can help
managers to break free from traditional notions about functional
barriers and ultimately lead to improved decision making and problem
solving (Huang et al. 2011). The BSC framework also does not provide the
quantitative and qualitative indicators how much each perspective
contributes, even on the relative importance weight for each perspective
and its corresponding indicators. However, the BSC framework does not
provide guidance as to how these weights should be computed. Youngblood
and Collins (2003) proposed that although the BSC provides valuable
feedback on a variety of performance metrics, but those metrics did not
consider the relative importance weigh and the issue of interaction and
trade-offs between metrics and for these reasons quantities methods like
MCDM methods applied with BSC.
The first private universities were established about 20 years ago
in Iran and today there are more than 250 well-known institutes and
universities in Iran. In this research BSC applies for evaluating of
Iranian private universities that there is no any research about
evaluation of these universities in Iran. The aim of this research is
only to identify important indices in this area. The literature review
revealed that MCDM methods in many researches were using (Fuzzy) AHP,
(Fuzzy) ANP for calculating the weights of indices (Dytczak, Ginda 2009;
Garcia, Melon et al. 2010; Azimi et al. 2011; Timoshenko 2008) and in
some researches they were used DEMATEL method base on cause and effect
relation between perspectives and indices. There are numerous researches
about applications of MCDM methods to BSC in many areas but there is not
any research about evaluating of private universities in Iran. In this
research three MCDM methods applied for evaluating of private
universities. At first DEMATEL used for evaluating cause and effect
relations between perspectives of BSC and in next step ANP applied for
identifying important criteria and weights of them and finally VIKOR
applied for comparing selected universities as case study and rank them.
The process of this research is shown in Figure 1.
[FIGURE 1 OMITTED]
2. Literature review
Owing to its ability to assist organizations or firms in selecting
among alternative missions/visions, selecting among alternative
strategies, and allocating resources to implement organizational
strategies and objectives, AHP has been successfully applied in numerous
BSC studies, including Huang (2009), Kim, H. S. and Kim, Y. G. (2009),
Varma et al. (2008), Chan (2006), Leung et al. (2006), Fletcher and
Smith (2004), Reisinger et al. (2003), Stewart and Mohammed (2001), and
Liberatore and Miller (1998). AHP is a method enabling evaluation of
both qualitative and quantitative variations in evaluating problems
together.
AHP and ANP were used in developing the analytical structure of BSC
model, which are multiple-criteria decision-making methods. AHP is a
multiple-criteria decision-making method developed by Saaty (1996). The
AHP method assumes that the factors presented in the hierarchical
structure are independent; however, it assumes that it may be
inappropriate in light of certain internal and external environment
effects. Therefore, it is necessary to employ of analytic network
Process (ANP) method (Lee 2007). The traditional financial method cannot
fully reflect the performance of enterprises, as a result of which the
Balanced Scorecard (BSC) method was developed. However, BSC also has
some disadvantages. By giving power weights on indicators, ANP method
can make up those disadvantages (Lee 2007). In ANP the hierarchical
relation between criteria and alternatives is generalized to networks.
Many decision problems cannot be structured hierarchically, because they
involve the interaction and dependence of high-level elements on
lower-level elements (Saaty 2003). ANP uses to analyze the relative
weights of performance indices.
The Multi-criteria Optimization and Compromise Solution (called
VIKOR) is a suitable tool to evaluate each alternative for each
criterion function (Opricovic 1998; Opricovic, Tzeng 2004, 2007; Tzeng
et al. 2005). The concept of VIKOR is based on the compromise
programming of MCDM by comparing the measure of "closeness" to
the "ideal" alternative. The multi-criteria measure for
compromise ranking is developed from the Lp-metric that is used as an
aggregating function in compromise programming (Yu 1973; Zeleny 1982).
The most popular MCDM methods, VIKOR and TOPSIS (Technique for Order
Preference by Similarity to Ideal Solution), both apply the concept of
compromise to solve the competing problem among the evaluation criteria
and then rank the order of the alternatives (Opricovic, Tzeng 2004,
2007). However, the TOPSIS method is used to provide information on how
to improve the gaps among the criteria so as to achieve the
desired/aspired level and it cannot be used for ranking purpose due to
its blind point proven by Opricovic and Tzeng (2004).
The DEMATEL method is applied to determine causal relationships and
mutual influence among perspective (Wu et al. 2011). The process for
building a strategy map could be viewed in a general body of a unified
group decision making context. If we see the strategy map, as a
structural modeling framework for making the cause and effect
relationships among the strategic objectives, it is possible to deploy
DEMATEL as a framework for structural modeling approach subject to the
problem. The DEMATEL method gathers collective knowledge to capture the
casual relationships between strategic criteria (Jassbi et al. 2011).
DEMATEL was used for cause and effect relationship in each perspective
of BSC for identifying the most important indices (Safaei Ghadikolaei et
al. 2011).
Table 1 was shown a brief review of past researches about MCDM
methods and BSC together. Hashemkhani Zolfani and Radfar (2011)
presented a review article about selecting best hybrid models of MCDM
methods and BSC that results demonstrate ANP and VIKOR are better than
AHP and TOPSIS for joining to BSC and DEMATEL is appropriate for
calculating cause and effect relations among perspectives.
3. Methodology
3.1. Experts information
In this paper, 57 criteria were selected for establishing a BSC
framework for private universities. We selected 30 experts for this
research with target sampling. After a questionnaire, 22 criteria were
selected for establishing BSC for universities. Information about
experts is shown in Table 2.
3.2. Selected criteria for establishing BSC
Targets of plans and purposes (Duqrette, Stowe 1993) stated that
performance indices are a kind of tool or indicators which are used for
assessing performance of organizations. They could be quantification
information and also could be a qualitative written description.
Therefore, the selection of criteria is very significant for assessing
the operating performance of organizations to achieve effective
operational management and raise the efficiency of operation and create
advantages and values to organizations.
3.3. DEMATEL method
The DEMATEL, originated from the Geneva Research Centre of the
Battelle Memorial Institute (Fontela, Gabus 1976; Gabus, Fontela 1973),
aims to convert the relationship between the causes and effects of
criteria into an intelligible structural model of the system (Liou et
al. 2008). In a totally interdependent system, all criteria of the
system are mutually related, directly or indirectly; thus, any
interference with one of the criteria affects all the others, so it is
difficult to find priorities for decision-making (Tzeng et al. 2007).
The DEMATEL method is briefly described as follows:
Step 1: Compute the average matrix. Each respondent was asked to
evaluate the direct influence between any two factors by an integer
score ranging from 0, 1, 2, and 3, representing "no
influence", "low influence", "medium
influence", and "high influence", respectively. The
notation of [x.sub.ij] indicates the degree to which the respondent
believes factor I affects factor j. For i = j, the diagonal elements are
set to zero. For each respondent, an n x n non-negative matrix can be
established as [X.sub.k] = [[x.sup.k.sub.ij]] where k is the number of
respondents with 1 [less than or equal to] k. H, and n is the number of
factors. Thus, [X.sup.1], [X.sup.2], [X.sub.3], ..., [X.sup.H] are the
matrices from H respondents. To incorporate all opinions from H
respondents, the average matrix A = [[a.sub.ij]] can be constructed as
follows:
[a.sub.ij] = [1/H] [H.summation over (k=1)] [x.sup.k.sub.ij]. (1)
Step 2: Calculate the normalized initial direct-relation matrix.
Normalize initial direct relation matrix D by D = A. S, where
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. Each element in
matrix D falls between zero and one.
Step 3: Calculate the total relation matrix. The total relation
matrix T is defined as T = D[(I D).sup.-1], where I is the identity
matrix. Define r and c be n x 1 and 1 x n vectors representing the sum
of rows and sum of columns of the total relation matrix T, respectively.
Suppose [r.sub.i] be the sum of i throw in matrix T, then [r.sub.i]
summarizes both direct and indirect effects given by factor i to the
other factors. If [c.sub.j] denotes the sum of j th column in matrix T,
then [c.sub.j] shows both direct and indirect effects by factor j from
the other factors. When j = i, the sum ([r.sub.i] + [c.sub.j]) shows the
total effects given and received by factor i. That is, ([r.sub.i] +
[c.sub.j]) indicates the degree of importance that factor i plays in the
entire system. On the contrary, the difference ([r.sub.i] - [c.sub.j])
depicts the net effect that factor i, contributes to the system.
Specifically, if ([r.sub.i] - [c.sub.j]) is positive, factor i is a net
cause, while
factor i is a net receiver or result if ([r.sub.i] - [c.sub.j]) is
negative.
Step 4: Set up a threshold value to obtain the digraph. Since
matrix T provides information on how one factor affects another, it is
necessary for a decision maker to set up a threshold value to filter out
some negligible effects, in doing so, only the effects greater than the
threshold value would be chosen and shown in digraph. In this study, the
threshold value is set up by computing the average of the elements in
matrix T. The digraph can be acquired by mapping the dataset of (r + c,
r - c).
3.4. The ANP method
ANP, also introduced by Saaty, is a generalization of the AHP.
Saaty (1996) suggested the use of AHP to solve the problem of
independence on alternatives or criteria, and the use of ANP to solve
the problem of dependence among alternatives or criteria. Many
decision-making problems cannot be structured hierarchically because
they involve the interaction and dependence of higher level elements on
lower level elements. This is a network system.
The process of ANP involves three sub steps and shown as follows
(Shyur 2006):
Step 1: Without assuming the interdependence among criteria, the
decision makers are asked to evaluate all proposed criteria pair wise.
The responses were presented numerically and scaled on the basis of
Saaty's 1-9 scale. Once the pair wise comparisons are completed,
the local weight vector [W.sub.1] is computed as the unique solution to
A[W.sub.1] = [[lambda].sub.max][W.sub.1]. (2)
Step 2: Where [[lambda].sub.max] is the largest eigen value of pair
wise comparison matrix A, The obtained vector is further normalized by
dividing each value by its column total to represent the normalized
local weight vector [W.sub.2], all the criteria on each other by using
pair wise comparisons as well. These pair wise comparison matrices are
needed to identify the relative impacts of criteria interdependent
relationships. The normalized principal eigenvectors for these matrices
are calculated and shown as column component in interdependence weight
matrix of criteria B, where zeros are assigned to the eigenvector
weights of the criteria from which a given criterion is given.
Step 3: Now we can obtain the interdependence weights of the
criteria by synthesizing the results from previous two steps as follows:
[W.sub.C] = [BW.sub.2]T. (3)
3.5. VIKOR method
3.5.1. Introduction to VIKOR
The VIKOR method is a compromise MADM method, developed by
Opricovic and Tzeng (Opricovic 1998; Opricovic, Tzeng 2002) started from
the form of Lp-metric:
[L.sub.pi] = [{[n.summation over (j=1)] [[[w.sub.j]([f.sup.*.sub.j]
- [f.sub.ij])/ ([f.sup.*.sub.ij] - [f.sup.-.sub.j])].sup.p]}.sup.1/p], 1
[less than or equal to] p [less than or equal to] +[infinity] = 1, 2,
..., I.
The VIKOR method can provide a maximum "group utility"
for the "majority" and a minimum of an individual regret for
the "opponent" (Opricovic 1998; Opricovic, Tzeng 2002, 2004).
3.5.2. VIKOR steps
1) Calculate the normalized value:
[f.sub.ij] = [x.sub.ij]/[square root of
[[summation].sup.n.sub.j=1]], i = 1, 2, ...,m; j = 1, 2, ..., n. (4)
2) Determine the best and worst values:
For all the attribute functions the best value was [f.sup.*.sub.j]
and the worst value was [f.sup.-.sub.j] that is, for attribute J = 1 -
n, we get formulas (2) and (3)
[f.sup.*.sub.j] = max [f.sub.ij], i = 1, 2 ... m, (5)
[f.sup.-.sub.j] = min [f.sub.ij], i = 1, 2 ... m, (6)
where [f.sup.*.sub.j] is the positive ideal solution for the jth
criteria, [f.sup.-.sub.j] is the negative ideal solution for the jth
criteria. If one associates all [f.sup.*.sub.j] one will have the
optimal combination, which gets the highest scores, the same as
[f.sup.-.sub.j].
3) Determine the weights of attributes:
The weights of attribute should be calculated to express their
relative importance.
4) Compute the distance of alternatives to ideal solution:
This step is to calculate the distance from each alternative to the
positive ideal solution and then get the sum to obtain the final value
according to formulas (7) and (8).
[S.sub.i] = [n.summation over (j=1)] [w.sub.j]([f.sup.*.sub.j] -
[f.sub.ij])/[[f.sup.*.sub.j] - [f.sup.-.sub.j]], (7)
[R.sub.i] = [max.sub.j] [[w.sub.j] ([f.sup.*.sub.j] -
[f.sub.ij])/[[f.sup.*.sub.j] - [f.sup.-.sub.j]]], (8)
where [S.sub.i] represents the distance rate of the zth alternative
to the positive ideal solution (best combination), [R.sub.i] represents
the distance rate of the zth alternative to the negative ideal solution
(worst combination). The excellence ranking will be based on [S.sub.i]
values and the worst rankings will be based on [R.sub.i] values. In
other words, [S.sub.i], [R.sub.i] indicate [L.sub.1i] and [L.sub.0i] of
[L.sub.p]--metric respectively.
5) Calculate the VIKOR values [Q.sub.i] for z = 1, 2, ..., m, which
are defined as:
[Q.sub.i] = v [[[S.sub.i] - [S.sup.*]]/[[S.sup.-] - [S.sup.*]]] +
(1 - v)[[[R.sub.i] - [R.sup.*]]/[[R.sup.-] - [R.sup.*]]], (9)
where [S.sup.-] = [max.sub.i][S.sub.i], [S.sup.*] =
[min.sub.i][S.sub.i], [R.sup.-] = [max.sub.i][R.sub.i], [R.sup.*] =
[min.sub.i][R.sub.i], and v is the weight of the strategy of "the
majority of criteria" (or "the maximum group utility").
[(S - [S.sup.*])/ ([S.sup.-] - [S.sup.*])] represents the distance rate
from the positive ideal solution of the zth alternative's
achievements. In other words, the majority agrees to use the rate of the
zth. [(R - [R.sup.*])/([S.sup.-] - [R.sup.*])] represents the distance
rate from the negative ideal solution of the zth alternative; this means
the majority disagree with the rate of the zth alternative. Thus, when
the v is larger (> 0.5), the index of [Q.sub.i] will tend to majority
agreement; when v is less (< 0.5), the index [Q.sub.i] will indicate
majority negative attitude; in general, v = 0.5, i.e. compromise
attitude of evaluation experts.
6) Rank the alternatives by [Q.sub.i] values:
According to the [Q.sub.i] values calculated by step (4), we can
rank the alternatives and to make-decision.
4. Assessing the performance of the private universities of Iran
We employ four perspectives as a framework for assessing the
standards of performance (Table 3). Based on this framework, the
research uses DEMATEL for cause and effect relations between
perspectives, ANP to weight the indexes and VIKOR to assess the
performance of the five private universities that established more than
ten years and selected as case study.
4.1. DEMATEL results
The results of cause and effect relations of perspectives presented
in Tables 4 and 5 (Fig. 2). Table 4 show results of financial
perspective; Table 5 shows results of customer perspective; Table 6 show
results of internal process perspective and Table 7 illustrate results
of learning & growth perspective. In this section used ideas of all
30 experts of Table 2.
[FIGURE 2 OMITTED]
4.2. ANP results
The results of the ANP demonstrate in Tables 7 and 8 which
presented results of indices in perspectives. In this section and VIKOR
results used 8 experts' ideas that Information about experts is
shown in Table 6.
Final results demonstrate clearly in Table 8 with specific
information.
As the results in Table 8 shown four important indices are in
customer perspective that describes that customer perspective in the
most important perspective in BSC for private universities. There is a
meaning relation between customer and internal process perspectives
because results of DEMATEL method shown that internal process is the
most effective perspective in BSC. In section five (conclusion), we will
describe more about ANP results.
4.3. VIKOR results
In this section according to results of results of ANP, VIKOR
applied for final ranking of universities that are: 1. Imam Reza
University (Mashhad), 2. Shomal University (Amol), 3. Shaikh bahaei
University (Isfahan), 4. Mazandaran University of Science and
Technology, 5. University of Science and Culture (Tehran). In this part
according to section 4.2 and Table 6, eight experts participate in
decision making. Life of private universities in Iran is less than 25
years old and that means these universities are so weak in
infrastructure and facilities and they need more time to become top
universities in competitive world. Most of these new private
universities are small and are called Institute of higher education and
most of them are less than 10 years old and it means this research
selected universities that are more than 10 years old and are fairly
developed in comparison with best universities of Iran and they are just
5 universities that selected as case study of this research because this
kind of university didn't develop fairly in Iran and authors
selected the best developed private universities in this research. The
information about decision matrix of VIKOR method is shown in Table 9
and it is clear that information of decision matrix is based on group
decision making and finally final results and ranking of alternatives
based on VIKOR presented in Table 10.
According to Table 10, Shomal University is best private university
and second university according to the ranking is the University of
Science and Culture, the third university is the University of Science
and Technology, Shaikh Bahai University and Imam Reza University are at
the bottom of the research.
5. Conclusions and discussions
By summarizing, this research has two different groups of experts
that participated in two section of this article. First group include 30
experts that participated for selecting final model of BSC and indices
and also for DEMATEL. Second group include 8 experts that help us for
ANP and VIKOR section. The final model of BSC for private universities
is illustrated in Table 3 that consist of 22 indices in perspectives.
Results of DEMATEL that has been shown in Table 5, describe that
Internal Process is the most effective perspective on other perspectives
of BSC in among perspectives that universities should concentrate on
that more than always because this perspective has a great influence on
other perspectives. Weights and ranking of indices has been shown in
Table 8 that results describe that (1. Brand, 2. Academic Excellence, 3.
Product Quality, 4. Student Satisfaction, and 5. Budget Control) are
five important indices of BSC for universities. One other point of ANP
section is customer perspective is the most important perspective in BSC
and it means that criteria of this perspective are more important than
other perspectives that we can find out there is a clear relation exists
between Internal Process and Customer perspectives because from the base
internal process perspective prepared to develop customer perspective.
Finally VIKOR applied for comparison universities that selected as a
case study and ranked them. Results have been shown in Table 9 (1.
Shomal University, 2. University of Scinece and Culture, 3. Mazandaran
University of Science and Technology, 4. Shaikh Bahaei University, and
5. Imam Reza University).
Authors suggest that (1) each student likes to study in a famous
and high quality university because it gives them the sense of
confidence and they can be more relaxed about their future. Authors
suggest that to these universities develop their plans with other
organizations and industries. International participates like held
international conferences can helpful for developing brand of
universities. (2) Academic excellence is an aim while establishing each
university and some important points exist that universities develop
their brands and can hire better academic staff and this plan can be
helpful for attraction students with better qualities. Appropriate
relation between private universities with high quality universities in
country and world is another plan for developing their brands. (3)
Hiring expert personnel in higher education management, educational
class for developing human resources are effective ways in educational
planning management and have good influence to increase level of Product
Quality. (4) Private universities are funded with fees that student pay
for their educations then we can see logical relation between student
satisfaction and existence of these universities. There are many factors
which have been influenced in this research and out of this research
that are not related to this research. (5) Budget control is one of
easiest and important factors of existence and development of an
organization. Budget control should be assigned with strategic aims and
according to situation; aims policy of organization should be used.
Authors also suggest that in future researches other new methods
applied with BSC like SWARA (Kersuliene et al. 2010) that can be used
rather than ANP. Results of this research can be comparisons with Fuzzy
ANP and Fuzzy VIKOR. Finally this research can be useful as a framework
for private universities in Iran and all around the world.
doi: 10.3846/16111699.2012.665383
Caption: Fig. 1: Performance evaluation framework of research
Caption: Fig. 2. Cause and effect diagram of perspectives
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Sarfaraz Hashemkhani Zolfani (1), Abdolhamid Safaei Ghadikolaei (2)
(1) Institute of Internet and Intelligent Technologies, Vilnius
Gediminas Technical University, Sauletekio al. 11, LT-10223 Vilnius,
Lithuania Department of Industrial Engineering, Shomal University, P.O.
Box 731, Amol, Iran
(2) Department of Industrial Management, Mazandaran University,
P.O. Box 416, Babolsar, Iran E-mils:
[email protected]
(corresponding author);
[email protected]
Received 04 November 2011; accepted 07 February 2012
Sarfaraz HASHEMKHANI ZOLFANI. BSc in Industrial Management from
Shomal University, Iran. MSc in Industrial Engineering--System
Management and Productivity in Shomal University, Iran. Works at the
Research Institute of the Internet and Intelligent Technologies, Vilnius
Gediminas Technical University, Lithuania. The member of EURO Working
Group OR in Sustainable Development and Civil Engineering. The author of
more than 40 scientific papers presented, published or reviewed at/for
International Conferences and Journals (including ISI-cited
publications). His research interests include: Performance Evaluation,
Strategic Management, Decision-making Theory, Supply Chain Management
and (Fuzzy) Multi Criteria Decision Making.
Abdolhamid SAFAEI GHADIKOLAEI. Assistant Professor of Mazandaran
University, Babolsar, Iran. He got PhD from Tarbiat Modares University
in Industrial Management-Production Management (2000). He is author of
more than 25 scientific papers. His research interests include: Supply
Chain Management, Production Management, (Fuzzy) Multi Criteria Decision
Making.
Table 1. Review of MCDM methods with BSC (After 2006)
Authors Methods Topic Year
Wu et al. DEMATEL, Performance evaluation of 2011
ANP, VIKOR, extension education centers
BSC in universities
Jassbi et Fuzzy Modeling cause and effect 2011
al. DEMATEL, BSC relationships
Safaei DEMATEL Cause and effect relations of 2011
Ghadikolaei BSC in Universities of Iran
et al.
Amiran et Fuzzy AHP, Evaluating performance of 2011
al. Fuzzy TOPSIS steel industries
Shaverdi et Fuzzy AHP, Performance evaluation of 2011
al. TOPSIS, private banking sector
VIKOR,
ELECTRE
Fouladgar et Fuzzy AHP, Prioritizing strategies of 2011
al. Fuzzy TOPSIS the Iranian mining sector
Tseng Fuzzy ANP, Implementation and 2010
DEMATEL, BSC performance evaluation using
the fuzzy network Balanced
Scorecard
Yuksel & Fuzzy ANP, Using the fuzzy analytic 2010
Dagdeviren BSC network process (ANP) for
Balanced Scorecard
Fasanghari TOPSIS, BSC Ranking the Information and
et al. Communication Technology 2009
Research Centers of Iran
Mao et al. TOPSIS, BSC Information system selection 2009
Wu et al. Fuzzy AHP, Evaluating banking 2009
TOPSIS, performance
VIKOR, SAW,
BSC
Wang & Xia Fuzzy AHP, Evaluating performance of a
BSC software company based on 2009
knowledge management
He et al. TOPSIS, BSC The performance evaluation of 2009
ERP application
Tsai et al. DEMATEL, The sustainability Balanced
ANP, ZOGP, Scorecard as a framework for 2009
BSC selecting socially
responsible investment
Cebeci Fuzzy AHP, Selecting ERP systems 2009
BSC
Mehregan & TOPSIS, BSC Evaluate the Best 's Iranian 2008
Dehghan Business Schools
Nayeri
Lee et al. Fuzzy AHP, Evaluating performance of IT 2008
BSC department in the
manufacturing industry
Lee AHP, ANP, A method of performance 2007
BSC evaluation by using the
analytic network process and
Balanced Scorecard
Haghshenas Fuzzy AHP, Performance Evaluation of IT 2007
et al. BSC
Thakkar et ANP, BSC Development of a Balanced 2006
al. Scorecard
Leung et al. AHP, ANP, Implementing the Balanced 2006
BSC Scorecard using the analytic
Hierarchy Process & the
Analytic Network Process
Table 2. Background information of experts
Category/Classification No.
Working background
Academic field 13
Government unit 17
Education Level
Bachelor 12
Master 10
PhD 8
Sex
Male 19
Woman 11
Table 3. Strategic objectives and performance measures for none
governmental universities
Perspective/ Definition
performance
indices
Financial (F)
F1. Cost control Decreasing direct cost of Bhagwat &
products and services; Sharma (2007)
reducing indirect cost and Kaplan &
sharing sources with other Norton (1996)
units
F2. Budget control Ratio of budget use (fir Bhagwat &
planned projects) accounted Sharma (2007)
for the total regularly
F3. Fund raising Building endowment/fund Farid et al.
raising/annual giving (2008)
F4. Scientific Academic excellence in various Farid et al.
research sciences (2008)
excellence
F5. Expanding Expanding breakthrough Kent Strategy
breakthrough research & creative endeavors Map
(Hashemkhani
Zolfani, Safaei
Ghadikolaei
2012)
Customer (C)
C1. Product Quality management of Bhagwat &
quality curriculums Sharma (2007)
C2. Student Ability to get access to Farid et al.
satisfaction "needed" courses and ease in (2008)
getting "good" job
C3. Academic Quality of students admitted Farid et al.
excellence and quality of faculty (2008)
C4. Service to Adequacy of participation in Farid et al.
the university campus-wide activities (2008)
C5. Brand Reputation of university Mehregan &
Nayeri (2008)
Internal process (P)
P1. Customized If there are new courses or Kaplan & Norton
courses services that are created (1996)
according to the demands of
potential students If there
are periodic reviews of
operational
P2. Operational business processes for Kaplan & Norton
Business process improvement in order to close (1996)
to the market and meet
students' needs
P3. Teaching If programs are assessed with Kaplan & Norton
quality evaluation teaching quality evaluation (1996)
regularly
P4. Currency of Contacts with business and Farid et al.
faculty and industry and utilization rate (2008)
classroom of multimedia in classroom
material/
experiences
P5. Quality Faculty credentials, faculty Farid et al.
faculty appraisals, endowed chairs, (2008)
faculty development plans
P6. Engaging the Improve online engagement of Kent Strategy
world beyond the international students/alumni Map
campus (Hashemkhani
Zolfani, Safaei
Ghadikolaei
2012)
Learning and growth (L)
L1. Faculty Investment for research, Farid et al.
development travel, library, computer (2008)
hardware/software teaching
assessments
L2. Teaching/ Development of assessment Farid et al.
learning device/ technique for each (2008)
innovations innovation
L3. Adequate Adequacy of classroom and Farid et al.
physical equipment facilities for (2008)
facilities providing globally relevant
management education
L4. Establish Evaluation of strategic Farid et al.
broad-based and planning (2008)
continuous
strategic
planning process
L5. Investment Plan for sustainable growth Cardiff
Strategy Map
(Hashemkhani
Zolfani, Safaei
Ghadikolaei
2012)
L6. Information Develop distinctive physical & Cardiff
Infrastructure virtual environments that Strategy Map
foster cohesion & excellence (Hashemkhani
for staff, students & Zolfani, Safaei
collaborators Ghadikolaei
2012)
Table 4. The initial influence matrix A for perspectives
Financial Customer Internal Learning
Business & growth
Financial 0 1.86 1.9 1.93
Customer 2.36 0 1.93 1.8
Internal Process 2.13 2.4 0 1.9
Learning & growth 1.86 1.93 2.23 0
Table 5. The total-influence matrix T for
perspectives sum of influences given and
received on each criterion
D+R D-R
F 31.7986(3) -1.3678
C 32.2327(2) -0.2273
P 32.6454(1) 0.762
L 31.0273(4) 0.8331
Table 6. Background information of experts
participated in ANP and VIKOR
Category/Classification No.
Working background
Academic field 7
Government unit 1
Education Level
PhD 8
Sex
Male 6
Woman 2
Table 7. Limiting supermatrix
F1 F2 F3 F4 F5
F1 0.0578 0.0578 0.0578 0.0578 0.0578
F2 0.0744 0.0744 0.0744 0.0744 0.0744
F3 0.0485 0.0485 0.0485 0.0485 0.0485
F4 0.0668 0.0668 0.0668 0.0668 0.0668
F5 0.0483 0.0483 0.0483 0.0483 0.0483
C1 0.0806 0.0806 0.0806 0.0806 0.0806
C2 0.0775 0.0775 0.0775 0.0775 0.0775
C3 0.0844 0.0844 0.0844 0.0844 0.0844
C4 0.0181 0.0181 0.0181 0.0181 0.0181
C5 0.0871 0.0871 0.0871 0.0871 0.0871
P1 0.0584 0.0584 0.0584 0.0584 0.0584
P2 0.0051 0.0051 0.0051 0.0051 0.0051
P3 0.0089 0.0089 0.0089 0.0089 0.0089
P4 0.0283 0.0283 0.0283 0.0283 0.0283
P5 0.0705 0.0705 0.0705 0.0705 0.0705
P6 0.0627 0.0627 0.0627 0.0627 0.0627
11 0.0314 0.0314 0.0314 0.0314 0.0314
12 0.0097 0.0097 0.0097 0.0097 0.0097
13 0.0273 0.0273 0.0273 0.0273 0.0273
14 0.0022 0.0022 0.0022 0.0022 0.0022
15 0.0192 0.0192 0.0192 0.0192 0.0192
16 0.0328 0.0328 0.0328 0.0328 0.0328
C1 C2 C3 C4 C5
F1 0.0578 0.0578 0.0578 0.0578 0.0578
F2 0.0744 0.0744 0.0744 0.0744 0.0744
F3 0.0485 0.0485 0.0485 0.0485 0.0485
F4 0.0668 0.0668 0.0668 0.0668 0.0668
F5 0.0483 0.0483 0.0483 0.0483 0.0483
C1 0.0806 0.0806 0.0806 0.0806 0.0806
C2 0.0775 0.0775 0.0775 0.0775 0.0775
C3 0.0844 0.0844 0.0844 0.0844 0.0844
C4 0.0181 0.0181 0.0181 0.0181 0.0181
C5 0.0871 0.0871 0.0871 0.0871 0.0871
P1 0.0584 0.0584 0.0584 0.0584 0.0584
P2 0.0051 0.0051 0.0051 0.0051 0.0051
P3 0.0089 0.0089 0.0089 0.0089 0.0089
P4 0.0283 0.0283 0.0283 0.0283 0.0283
P5 0.0705 0.0705 0.0705 0.0705 0.0705
P6 0.0627 0.0627 0.0627 0.0627 0.0627
11 0.0314 0.0314 0.0314 0.0314 0.0314
12 0.0097 0.0097 0.0097 0.0097 0.0097
13 0.0273 0.0273 0.0273 0.0273 0.0273
14 0.0022 0.0022 0.0022 0.0022 0.0022
15 0.0192 0.0192 0.0192 0.0192 0.0192
16 0.0328 0.0328 0.0328 0.0328 0.0328
P1 P2 P3 P4 P5 P6
F1 0.0578 0.0578 0.0578 0.0578 0.0578 0.0578
F2 0.0744 0.0744 0.0744 0.0744 0.0744 0.0744
F3 0.0485 0.0485 0.0485 0.0485 0.0485 0.0485
F4 0.0668 0.0668 0.0668 0.0668 0.0668 0.0668
F5 0.0483 0.0483 0.0483 0.0483 0.0483 0.0483
C1 0.0806 0.0806 0.0806 0.0806 0.0806 0.0806
C2 0.0775 0.0775 0.0775 0.0775 0.0775 0.0775
C3 0.0844 0.0844 0.0844 0.0844 0.0844 0.0844
C4 0.0181 0.0181 0.0181 0.0181 0.0181 0.0181
C5 0.0871 0.0871 0.0871 0.0871 0.0871 0.0871
P1 0.0584 0.0584 0.0584 0.0584 0.0584 0.0584
P2 0.0051 0.0051 0.0051 0.0051 0.0051 0.0051
P3 0.0089 0.0089 0.0089 0.0089 0.0089 0.0089
P4 0.0283 0.0283 0.0283 0.0283 0.0283 0.0283
P5 0.0705 0.0705 0.0705 0.0705 0.0705 0.0705
P6 0.0627 0.0627 0.0627 0.0627 0.0627 0.0627
11 0.0314 0.0314 0.0314 0.0314 0.0314 0.0314
12 0.0097 0.0097 0.0097 0.0097 0.0097 0.0097
13 0.0273 0.0273 0.0273 0.0273 0.0273 0.0273
14 0.0022 0.0022 0.0022 0.0022 0.0022 0.0022
15 0.0192 0.0192 0.0192 0.0192 0.0192 0.0192
16 0.0328 0.0328 0.0328 0.0328 0.0328 0.0328
L1 L2 L3 L4 L5 L6
F1 0.0578 0.0578 0.0578 0.0578 0.0578 0.0578
F2 0.0744 0.0744 0.0744 0.0744 0.0744 0.0744
F3 0.0485 0.0485 0.0485 0.0485 0.0485 0.0485
F4 0.0668 0.0668 0.0668 0.0668 0.0668 0.0668
F5 0.0483 0.0483 0.0483 0.0483 0.0483 0.0483
C1 0.0806 0.0806 0.0806 0.0806 0.0806 0.0806
C2 0.0775 0.0775 0.0775 0.0775 0.0775 0.0775
C3 0.0844 0.0844 0.0844 0.0844 0.0844 0.0844
C4 0.0181 0.0181 0.0181 0.0181 0.0181 0.0181
C5 0.0871 0.0871 0.0871 0.0871 0.0871 0.0871
P1 0.0584 0.0584 0.0584 0.0584 0.0584 0.0584
P2 0.0051 0.0051 0.0051 0.0051 0.0051 0.0051
P3 0.0089 0.0089 0.0089 0.0089 0.0089 0.0089
P4 0.0283 0.0283 0.0283 0.0283 0.0283 0.0283
P5 0.0705 0.0705 0.0705 0.0705 0.0705 0.0705
P6 0.0627 0.0627 0.0627 0.0627 0.0627 0.0627
11 0.0314 0.0314 0.0314 0.0314 0.0314 0.0314
12 0.0097 0.0097 0.0097 0.0097 0.0097 0.0097
13 0.0273 0.0273 0.0273 0.0273 0.0273 0.0273
14 0.0022 0.0022 0.0022 0.0022 0.0022 0.0022
15 0.0192 0.0192 0.0192 0.0192 0.0192 0.0192
16 0.0328 0.0328 0.0328 0.0328 0.0328 0.0328
Table 8. Relative weights of performance indices
Perspectives/Indices Relative Ranking
Weight
Financial (F) 0.2958 (2)
F1. Cost control 0.0578 10
F2. Budget control 0.0744 5
F3. Fund raising 0.0485 11
F4. Scientific research excellence 0.0668 7
F5. Expanding breakthrough 0.0483 12
Customer (C) 0.3477 (1)
C1. Product quality 0.0806 3
C2. Student satisfaction 0.0775 4
C3. Academic excellence 0.0844 2
C4. Service to the university 0.0181 18
C5. Brand 0.0871 1
Internal Process (P) 0.2379 (3)
P1. Customized courses 0.0584 9
P2. Operational business process 0.0051 21
P3. Teaching quality evaluation 0.0089 20
P4. Currency of faculty and classroom 0.0283 15
material/experiences
P5. Quality faculty 0.0705 6
P6. Engaging the world beyond the campus 0.0667 8
Learning & Growth (L) 0.1226 (4)
L1. Faculty development 0.0314 14
L2. Teaching/learning innovations 0.0097 19
L3. Adequate physical facilities 0.0273 16
L4. Establish broad-based and continuous 0.0022 22
strategic planning process
L5. Investment 0.0192 17
L6. Information infrastructure 0.0328 13
Table 9. Decision matrix
F1 F2 F3 F4 F5
Imam Reza University 7.6 7 7.2 5.6 6.8
([A.sub.1])
Shomal University 7.8 7.6 7.4 6.2 7.2
([A.sub.2])
Shaikh bahaei 7.8 7.4 7.2 6 6.4
University ([A.sub.3])
Mazandaran University 7.6 7.4 7.2 6.8 5.4
of Science and
Technology ([A.sub.4])
University of Science and 7.8 7.8 7.4 6.4 6.4
Culture ([A.sub.5])
C1 C2 C3 C4 C5
Imam Reza University 6 6 5 4.2 5.6
([A.sub.1])
Shomal University 6.4 6.4 5.6 5.2 6
([A.sub.2])
Shaikh bahaei 6 6 5.4 4.2 5.4
University ([A.sub.3])
Mazandaran University 6 6 5.6 4.2 5.8
of Science and
Technology ([A.sub.4])
University of Science and 6.2 6.4 5.8 5 6
Culture ([A.sub.5])
P1 P2 P3 P4 P5 P6
Imam Reza University 3.6 3.4 5 4.4 4.6 2.8
([A.sub.1])
Shomal University 4 4.2 5.2 4.8 5.4 3.8
([A.sub.2])
Shaikh bahaei 3.6 3.4 5 4.4 4.4 2.6
University ([A.sub.3])
Mazandaran University 4.2 3.2 5.2 4.6 4 4
of Science and
Technology ([A.sub.4])
University of Science and 4 4 5 4.6 4.4 3
Culture ([A.sub.5])
L1 L2 L3 L4 L5 L6
Imam Reza University 4.8 6 5 4.8 6.2 5
([A.sub.1])
Shomal University 5.2 5.2 6 5.4 7 5.8
([A.sub.2])
Shaikh bahaei 4.6 4.4 4.8 4.6 5.8 4.8
University ([A.sub.3])
Mazandaran University 4.2 4.4 4.4 4.6 5.6 4.8
of Science and
Technology ([A.sub.4])
University of Science and 4.6 5 5.4 4.6 6 5.6
Culture ([A.sub.5])
f * = [0.4518, 0.4685, 0.4546, 0.4894, 0.4974, 0.4675,
0.5243, 0.4727, 0.5075, 0.4654, 0.4831, 0.5131, 0.4576,
0.4705, 0.5268, 0.5441, 0.4957, 0.5329, 0.5211, 0.5020,
0.5099, 0.4970].
f- = [0.4402, 0.4204, 0.4422. 0.4030, 0.3733, 0.4382, 0.4916,
0.4075, 0.4099, 0.4189, 0.4141, 0.3909, 0.4400, 0.4312,
0.3902, 0.3536, 0.4003, 0.3908, 0.3821, 0.4276, 0.4079,
0.4114].
Table 10. Ultimate results and ranking of the alternatives
Alternatives [S.sub.i] [R.sub.i] [V.sub.i] [Q.sub.i] Ranking
[A.sub.1] 0.8390 0.884 0.5 0.1405 5
[A.sub.2] 0.1073 0.334 0.5 -0.25285 1
[A.sub.3] 0.7698 0.0871 0.5 0.1053 4
[A.sub.4] 0.7698 0.0775 0.5 0.0305 3
[A.sub.5] 0.2833 0.0503 0.5 -0.15462 2
