Constructing a musa model to determine priority factor of a servperf model.

Mathematical Theory and Modeling www.iiste.org
ISSN 2224-5804 (Paper) ISSN 2225-0522 (Online)
Vol.4, No.11, 2014
33
Constructing a MUSA Model to Determine Priority Factor of a
SERVPERF Model.
Sabri Ahmad1*
Loshini Thiruchelvam2
1. School of Informatics and Applied Mathematics, Faculty of Science and Technology, University
Malaysia Terengganu
2. School of Informatics and Applied Mathematics, Faculty of Science and Technology, University
Malaysia Terengganu
* E-mail of the corresponding author:losht_88@yahoo.com2
Abstract
The study aimed to determine the priority factor among the factors in a SERVPERF Model. The SERVPERF
Model explains the students’ satisfaction towards the quality of service provided by their hostel management.
Priority factor is the factor that is considered important by the customers, but they are not satisfied with the
service provided for that factor. A Multi-criteria Satisfaction Analysis (MUSA) Model is built based on ordinal
regression with linear programming approach. However, study found that the MUSA Model built is not stable
and could not interpret the data set used. This finding is consistent with the fact that MUSA Model does not
always give out an interpretable results..
Keywords: Multi-criteria Satisfaction Analysis (MUSA), Modified SERVPERF Model, Priority Factor, Ordinal
Regression, Linear Programming Approach.
1. Introduction
Quality matter is an important aspect in both the government and private organizations. Only if good quality of
service is provided, then the customers will be satisfied. On the other hand, satisfaction can be defined as the
assessment of customers towards the service they received. According to Tse & Wilton (1988), satisfaction is the
result of comparison between the service that customers expected with the service they receive at real. If the
service they receive is better than the one they expected, then customers are said to be satisfied and vice versa.
There are many type of models that can be used to measure customer satisfaction. Among them is the (Service
Performance) SERVPERF model by Cronin & Taylor (1992). This SERVPERF model have five dimensions,
namely Tangible, Responsive, Assurance, Reliability and Empathy. The dimensions describe the quality of
service received by the customer, with each have a specific explanation.
As there are five factors in the SERVPERF Model, the management need to take care all these factors in order to
provide a good quality of service, to ensure the students are satisfied with their service. However, it might be a
big challenge to the management to take care all the factors at the same time, and therefore the quality of their
service might decrease, causing customers not being satisfied and stop using their service. Therefore,
management should know which dimensions need the priority (Grigoroudis & Siskos, 2002). For this, factors
should be arranged according to their importance and satisfaction of the customers. Priority should be given to
the factor which is considered important by customers, but they are not satisfied with service provided for that
factor.
2.0 Literature Review
Arabatzis. & Grigoroudis (2010) carried out a study to investigate the visitors of Dadia-Lefkini-Souflion
National Park’satisfaction and perception towards the service provided and to analyze the gap between them.
The study aims to determine the factors that influences visitors satisfaction and to determine aspects that
need more attention and improvements by the management. The study uses primer data through surveys from
the visitors directly, with few aspects being touched in the survey. The aspects been discussed were on
aspects of nature, staffs’ service, infrastructure, recreational facilities, and information centre. Study
used Multi-criteria Satisfaction Analysis (MUSA) and Gap Analysis method. Findings show that 88% of the
visitors are satisfied with the service provided, with mainly they are satisfied with nature and staffs’service.

Vol.4, No.11, 2014
34
Grigoroudis et al. (2000) carried out a study entitled TELOS: A Computer Software to Evaluate Customers
Satisfaction. The study aimed to introduce TELOS as the software to construct MUSA Model. This TELOS
software only needs a brief data of respondents and it does not have any limit for on the number of scales and
levels of criteria to be used. Three different types of analysis can be done using this software, which are;
Descriptive Analysis, Global Exploratory Analysis (Average Global Satisfaction Index and Added Value Curve
for global satisfaction), and Partial Exploratory Analysis (weight of each criteria, Average Partial Satisfaction
Index, Added Value Curve for each criteria).
Ipsilandis et al. (2008) carried out a study entitled MUSA Approach on Evaluating an Education Program
Operation. The study’s objective is to identify the pattern of satisfaction on interaction and support received by
the Project Manager from three different group of shareholders. The group of shareholders differ from the
authority where Project Management Authority (PMA) is the highest group, followed by Project Organization
(PO). Both these groups are at the higher authority compared to the Project Manager. The third group of
shareholder is the Project Team (PT) who were the shareholders who helps out the manager in completing the
project. Study found that the Project Manager globally described a low satisfaction level for the interaction and
support he gained from these three goup of shareholders., especially from the Project Organization (PO).
Grigoroudis et al. (2008) carried out a study on Quality Evaluation of Customer Website: Application of
Satisfaction Benchmarking. The study aimed to identify the best website provider among the three companies
compared and was based on two objectives; to categorize criteria according to priority given by customers and
their satisfaction for each company. The first objective was achieved by building the Action Diagram involving
satisfaction of customers for each criteria, importance of each criteria and improvement effort needed for each
criteria. Second objective is achieved through the evaluation of the performance of each company, compared to
their competitors. Through this objective, advantages and disadvantages of each company compared to their
competitors can be identified. The study uses the basis MUSA model for the first objective and a modified
MUSA Model for the second objective. From the first Action Diagram, it was found that criteria `Physical
Efficiency’ is the priority factor/factor that needs the most improvement action. Based on the second Action
Diagram, it was found that the criteria `Animation’ and criteria `Technical Efficiency’ are advantage criteria for
the respective company, A and B. This is because both criteria are in the Waiting Quadrant. That is the
performance provided is low, but the quality of the criteria compared to competitors, is still higher compared to
the competitors.
3.0 Methadology
According to Siskos et al., 1998; Grigoroudis & Siskos, 2002, The MUSA (Multi-criteria Satisfaction Analysis)
is an analysis with multi-criteria preference disaggregation technique. The main objective of the MUSA method
is the disaggregation of individual judgments into a collective value function, and therefore it assumes that
customer’s global satisfaction depends on a set of criteria representing few service characteristics dimensions.
MUSA uses the ordinal-regression based approach for the assessment of a set of collective satisfaction functions,
which causes the global satisfaction criterion becomes as consistent as possible with customers-judgments.
Given customers’ global satisfaction Y, and partial satisfaction xi (according to the ith
criterion of ordinal scaling),
the method follows and additive collective value function Y* and a set of partial satisfaction (value) function xi*.
The goal of this method is to obtain the maximum consistency between the value function Y* and customers’
judgments, Y.
According to the modeling preference disaggregation approach and addition of a double-error variable, the
ordinal regression equation is :
,_________________________(1)

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Where, indicates the estimation of global value function Y*, and indicates the overestimation
and underestimation respectively.
Y* and , which are the global and partial satisfaction value respectively, are normalized in the interval of 0 to
100. Therefore, they are monotone function. Thus, mono-tonicity constraints for Y* and are removed, so
that the size of mathematical program could be reduced. For this, the transformation equations are used :
zm = y*m+1
– y*m
, for m= 1,2,…,α-1
dik = bixi
*k
for k = 1,2,…,αi-1 and i = 1,2,…,n
where y*m
is value of the ym
satisfaction level,
xi
*k
is the value of the xi
k
satisfaction level,
α and αi are the number of global and partial satisfaction value respectively.
Therefore, the basic estimation model can be written in the form of a linear program formulation, as below:
Stability Analysis
Considering that the MUSA method is based on a linear programming modeling, the stability analysis is
considered as a post-optimality analysis problem. As shown in Figure below, the post-optimal solution space is
defined by the polyhedron :
F
All the constraints of Linear Programming (LP),
-where is the optimal value of the objective function of LP, and is a small % of
Objective Function: Min

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Subject to
Given that,
), 0, for
4.0 Findings and Data Analysis
In This study uses SERVPERF Model, where this model has five criteria, therefore the weight of each criteria is
1/3. Both the variables in global satisfaction and satisfaction for each criteria used a likert scale of seven, so each
variable will have an increment of 16.67% in each level. The value of levels in global satisfaction variable is as
below:
The values for each criteria is as below:
(2)

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Therefore, based on equation (1), function value for the global satisfaction is estimated to be consistent with the
summation of partial satisfaction. Any differences between both satisfaction is covered by the introduction of
estimation error.
Next, MUSA Model was built using the Excel Solver software. However, it was found that the model could not
be interpreted as the solution was formed on the infeasible region. Therefore, modification is done with
introducing the preference threshold ( and ). However, this modification could not be done as the preference
threshold value will be negative even with a minimum increase of two. This condition does not obey the
equation 3 below, that is:
where,
(2 x (7-1)) x 22 = 264 > 100
Therefore, the model is modified by categorizing respondent according to gender. It is found that models of both
gender boys and girls could be interpreted, and it is located at the feasible region. However, the models were
found not to be stable as it only have the value of 100 at only one criteria and zero at all the other criteria. In
detailed categorization into original states (either East Coast, West Coast, and Sabah& Sarawak). However, even
after the categorization, the models were still found not to be stable. Therefore, it can be concluded that MUSA
Model could not be build for this data set. Table 9 till 18 (in Appendix Section) shows the result of MUSA Model
built for each category.
5.0 Conclusion
Study found that MUSA Model built is not stable and could not interpret the data set. The result of unstable
model is consistent with the theory that MUSA Model not necessarily could interpret data well, and there is
always a possibility to obtain an unstable model (Grigoroudis & Siskos, 2002). This conclusion is also supported
by the writing by Jao et al. (2007), where they did not obtain a stable model when used a data set of 70

Vol.4, No.11, 2014
38
respondents, but obtained stable models with 30 and 100 respondents. They discussed that MUSA Model uses
Least Absolute Deviation method to find the error in the model, where this method is based on iteration approach.
This approach gives more than one solution, and therefore causing unstable results/models.
Acknowledgements
The authors wish to thank the hostel management for allowing surveys to be taken from their students. This work
was supported in part by a grant from Ministry of Higher Education, under the MyBrain15 scheme.
References
Arabatzis, G. Grigoroudis, E. (2010). Visitors’ Satisfaction, Perception and Gap Analysis: The Case of
Dadia-Lefkini-Souflion National Park Forest Policy and Economics. 12(2010)163-172
Grigoroudis, E. & Siskos, Y. (2002). Preference Disaggregation For Measuring and Analyzing Customer
Satisfaction : The MUSA Method. European Journal of Operational Research 143(1), 148-170.
Grigoroudis, E. & Siskos, Y. (2010). MUSA: Multi-criteria Satisfaction Analysis (MUSA) & Extension
of the MUSA Method. In Customer Satisfaction Evaluation. ed. Hiller F.S., Price,C.C., pp 91-129. New York:
Springer Press.
Grigoroudis, E. Siskos, Y., & Saurais, O. (2000). TELOS: A Customer Satisfaction Evaluation Software.
Computers & Operations Research 27 (2000) 799-817
Ipsilandis, P.G., Samaras, G., & Mplanas, N. (2008). A MulticriteriaSatisfaction Analysis Approach
in the Assessment of Operational Programes. International Journal of Project Management 26(2008) 601-611
Joao, I.M., Carlos, A., Costa, B.e., & Figueira, J.R. (2007). An Alternative to the MUSA Method for
Customer Satisfaction Analysis. Centre of Management Studies of IST(CEG-IST).
Mihelis, G., Grigoroudis, E., Siskos, Y., Politis, Y., & Malandrakis, Y. (2001). Customer Satisfaction
Measurement in the Private BankSector. European Journal of Operational Research 130(2001) 347-360
Appendix

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