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Expert Systems with Applications 36 (2009) 5613–5619
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Expert Systems with Applications
journal homepage: www.elsevier.com/locate/eswa
Optimizing a marketing expert decision process for the private hotel
Chin-Tsai Lin *, Chuan Lee 1, Cheng-Shiung Wu 2
Graduate School of Management, Ming Chuan University, 250, Section 5, Chung Shan North Road, Taipei 11103, Taiwan, ROC
a r t i c l e
i n f o
Keywords:
Marketing expert decision process
Fuzzy analytic network process (fuzzy ANP)
Multiple criteria decision-making (MCDM)
Private hotel
a b s t r a c t
The study builds on the marketing strategy decision process for marketing experts. Marketing strategy
decision-making is necessary for marketing experts to determine a more efficient appropriate marketing
strategy. Selecting the best marketing strategy is a multiple criteria decision-making (MCDM) problem,
due to the complexity and difficulty of allocated specific resources and capabilities. This study provides
a five-step decision-making process to enable careful marketing strategy assessment, and contributes to
practical implementation for fuzzy analytic network process (fuzzy ANP) utilization by marketing experts
in a real industry. The results also provide marketing strategy guidance to practitioners for capturing
competitive advantage in elaborating specific and limited marketing resources. The proposed process
is easily understood and followed by marketing experts to determine appropriate marketing strategy.
Ó 2008 Elsevier Ltd. All rights reserved.
1. Introduction
Corporations must pay increased attention to creativity in generating strategic directions, rigorously evaluate strategic options
for achieving multiple and interdependent objectives, and maintaining a vision and focus to ensure effective resource utilization
(Wind, 1987). Technology utilization for altering the competitive
paradigm suggests intertwining computerization with marketing
activities offer critical advantages (Stone & Good, 2001), and firm
manipulated limited resources and capabilities among managerial
functions. Greater emphasis has been placed on marketing considerations in the managerial process, underscoring the important
role that marketing plays in contributing to a firm’s competitive
success (Brooksbank, Kirby, Tompson, & Taylor, 2003). Firm entrepreneurs should select a marketing strategy from a diverse range of
marketing strategies to pursue increased revenue and profits
simultaneously. Various strategic choices imply the need for reasonable implementation and control actions in a diverse set of
functional units. This emphasis is understandable for the current
era of marketing strategies where market forces are the main drivers of strategic decision-making (Cravens, 1998).
Since problems associated with strategic marketing system are
becoming increasingly complex, handling all problems using a single set of guidelines or decision model seems difficult. Multi-crite-
* Corresponding author. Tel.: +886 3 530 2588.
E-mail addresses: [email protected] (C.-T. Lin), [email protected] (C. Lee),
[email protected] (C.-S. Wu).
URL: http://web.ypu.edu.tw/prince/html/reports_01.htm (C.-T. Lin).
1
Tel.: +886 2 2881 2549.
2
Tel.: +886 2 28824564x2401; fax: +886 2 28809764.
0957-4174/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved.
doi:10.1016/j.eswa.2008.06.113
ria decision-making (MCDM) methods help reach important
decisions that cannot be made straightforwardly. The underlying
MCDM principle is that decisions should be based on multiple criteria (Cheng, Li, & Yu, 2005). Assessing competitive advantages of
specific firms is always problematic given the difficulty in deciding
marketing strategy.
Using a single set of guidelines or decision model is difficult
given the complex of problems associated with marketing strategic
system. The analytic network process (ANP) is a general ratio scale
theory that measures methodology influence dealing with
dependence and feedback (Saaty, 1996). Many traditional MCDM
methods are based on independence assumption. However, interdependent relationships between individual criterion in many situations are not completely independent (Shee, Taeng, & Tang,
2003). Many fields successfully apply the ANP method, such as
knowledge management (Wu & Lee, 2007), system development
(Chang, Wu, Lin, & Lin, 2007), process decision (Partovi, 2007), total
quality management (TQM) (Bayazit & Karpak, 2007), high-tech
architecture (Lin, Chiu, & Tsai, in press), and quality function development (QFD) (Kahraman, Ertay, & Büyüközkan, 2006) and so on.
Nevertheless, human judgment of events may be significantly
different based on individuals’ subjective perceptivity or personality, even when using the same words. Triangular fuzzy numbers
have been developed in order to express linguistic variables completely. However, the fuzzy approach is adopted to deal with possible uncertainty in subjective judgments. By assigning triangular
fuzzy numbers, Chang’s (1996) context analysis is employed to
identify relative importance of criteria and alternative weights
among criteria.
The current study employs the fuzzy ANP, given its advantages,
to offer firm practitioners and marketing experts a set of guidelines
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C.-T. Lin et al. / Expert Systems with Applications 36 (2009) 5613–5619
for designing and implementing competitive marketing strategies
by allocating appropriate resources. This study builds on the marketing expert decision-making process as a MCDM problem and
provides a five-step decision-making process to select an appropriate marketing strategy systematically. Hence, this study utilizes a
fuzzy ANP to obtain the relative criteria weights from subjective
judgments of private hotel experts. The proposed method significantly assists practitioners and marketing experts in assessing
marketing strategy alternatives, making it easily applicable for academic and commercial applications.
2. Proposed model
Given the complexity and uncertainty in our environment,
MCDM is a powerful decision-making tool to structure problem
clearly and systematically. Many studies relate to marketing strategy and marketing resources, but few deals with criteria for MCDM
marketing strategy. This study proposes a five-step process for
marketing strategy decision-making as illustrated in Fig. 1, following interviews with private hotel industry experts and practitioners and a review of related literatures. The first step constructs
the hierarchy and interdependent relationship among marketing
resources for marketing strategy. The second step determines pairwise comparisons with respect to marketing resources and marketing strategy. The third step computes criteria weights and
interdependent weights of marketing resources. Then this work
builds and solves the decision-making supermatrix according to
vectors of relative importance weights. Finally, it selects the best
marketing strategy. Detailed descriptions of each step are elaborated in each of the following sub-section.
Porter (1980) introduced a typology of three generic marketing
strategy alternatives for creating a defensible position and outperforming the competitor in a given industry, including overall cost
leadership, differentiation and focus (Panayides, 2004). Practitioners might be in a superior cost strategy position to achieve cost
decrement, when they find acquisition and development of necessary resources. The resource-based theory of the firm in differentiation strategy suggests that resource requirement similarity
among rival companies may increase competition (Barney, 2001).
Boyt and Harvey (1997) also states that differentiation through
offering superior customer service is especially important, while
Grant (1998) points out that successful product/service differenti-
ation is achieved through innovations and improvements across
different parts of the value chain. Panayides (2004) investigates,
based on Porter’s focus strategy, the impact of major marketing
thought and market segmentation as a fundamental precursor to
a focused strategy and important product-market strategy. Market
segmentation benefits could be widespread, ranging from understanding customer needs and delivering customer value to achieving competitive advantage and improved organizational
performance.
Many resources underpinning marketing activities could be
potentially significant advantage-generating resources. Hooley,
Greenley, Cadogan, and Fahy (2005) suggests the most interesting
criteria for determining marketing strategy. Encapsulated resources could gain market values as term marketing resources,
including market-based resources and marketing support resources. Marketing resources could be resources immediately deployed in the marketplace to create or maintain competitive
advantage, including customer linking capabilities, market innovation capabilities, human resource assets and reputational assets.
Marketing support resources on the other hand, serve primarily
to support marketing activities and contribute indirectly to competitive advantage, including managerial capabilities and market
orientation.
The large number of criteria usually considered in the marketing strategy evaluation process makes it very difficult for marketing experts. This study uses the five aspects as a skeleton and
synthesize other literatures and practical consideration to incorporate marketing resources proposed by Hooley et al. (2005), included managerial capabilities, customer linking capabilities,
market innovation capabilities, human resource assets and reputational assets, the details of which can be found in Table 1.
3. Fuzzy ANP method
Since marketing strategy evaluation criteria possesses diverse
significance and meanings each evaluation criterion cannot be
equally importance. Many methods could be utilized to determine
weights, such as the eigenvector method, weighed least square
method, entropy method, AHP, LINMAP (linear programming techniques for multidimensional of analysis preference) (Tsaur, Tzeng,
& Wang, 1997). However, the ANP is also a relatively new MCDM
method which can handle all kinds of interactions systematically
(Wu, in press). Applying the ANP could make marketing experts
overcome traditional analytic hierarchy process (AHP) method limitations using an easier one.
In-depth
interview
Expert
experience
Construct the hierarchy and
interdependence of model
Literature
review
Determine the pairwise
comparisons
Compute the weights of criteria
Build and solve the
supermatrix
Select the best marketing
strategy alternative
Fig. 1. Marketing expert decision-making process.
Table 1
The evaluation criteria and their related attributes
Criteria
Evaluation attributes
Management
capabilities
(MC)
Customer
linking
capabilities
(CLC)
Market
innovation
capabilities
(MIC)
Human
resource
assets
(HRA)
Reputation
assets (RA)
Financial condition, human resource effective, operation
management technology, and service management
Level of customer service, relationship with key target
customers, understanding customers’ needs and requirements,
creating relationships with customers, and maintaining and
enhancing relationships with customers
Ability to launch new products and services, and new product
and service development process effective
Levels of employee job satisfaction and levels of employee
retention
Company or brand name or reputation, and credibility with
customers
Note: According to Hooley et al. (2005).
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C.-T. Lin et al. / Expert Systems with Applications 36 (2009) 5613–5619
Even though ANP method benefits include easy operation and
integrating opinions of most decision makers, the conventional
ANP method does not express human thinking completely. However, decision makers usually feel more confident giving interval
judgments rather expressing judgments in single numeric values
(Erensal, Oncan, & Demircan, 2006). Determining marketing strategy is both a complex and extensive problem, and the fuzzy ANP
method development solves alternative selection and justification
problems. This work utilizes fuzzy ratios instead of crisp values to
handle the difficulty of assigning ratios and deriving criteria fuzzy
weights by the geometric mean method.
The evaluation model utilized the triangular fuzzy conversion
scale given in Table 2. Zadeh (1965) asserts that it is difficult for
conventional quantification to express reasonably situations,
which are apparently complex or hard to define. So, a linguistic
variable is necessary in such situation. This study combines the
computational technique with linguistic scales used by Chiou and
Tzeng (2001) and the following number scale defined by Lee, Chen,
and Chang (2008). The linguistic scales include ‘‘equally important,” ‘‘weakly important,” ‘‘essentially important,” ‘‘very strongly
important,” and ‘‘absolutely important” with respect to fuzzy level
scale. Each membership function of linguistic scale is defined by
three parameters of the symmetric triangular fuzzy number,
shown as Table 2.
Chang (1996) also develops the extent analysis method on fuzzy
AHP. Hence, the current study applies Chang’s extent analysis on
fuzzy ANP in marketing strategy selection, by allowing fuzzy numbers for pairwise comparisons and determining fuzzy weights.
3.1. Chang’s extent analysis method
The steps of Chang’s extent analysis approach, by integrating
the improvement proposed by Zhu, Jing, and Chang (1999), are
as follow. Let X = {x1, x2, . . . , x3} be an object set, and U =
{u1, u2,. . . , u3} be a goal set. According to Chang’s extent analysis,
each objects is toke and performed extent analysis for each goal,
gi, respectively. Therefore, m extent analysis values for each object
are obtained and shown as follows:
M 1gi ; M 2gi ; . . . ; M m
gi ;
i ¼ 1; 2; . . . ; n
ð1Þ
where all the M jgi ðj ¼ 1; 2; . . . ; mÞ are triangular fuzzy numbers
(TFNs) whose parameters are l (the least possible value), m (the
most possible value), and u (the largest possible value), respectively. A TFN is represented as (l, m, u).
The steps of the Chang’s extent analysis can be given as the followings:(1) The value of fuzzy synthetic extent with respect to the
ith object is defined as
Si ¼
m
X
M jgi "
n X
m
X
j¼1
i¼1
#1
Mjgi
ð2Þ
j¼1
where denotes the extended multiplication of two fuzzy numPm
j
bers. In order to obtain
j¼1 M gi , we perform the fuzzy addition
operation of m extent analysis values for a particular matrix such
that
m
X
M jgi ¼
m
X
j¼1
lj ;
j¼1
m
X
mj ;
m
X
j¼1
!
uj ;
i ¼ 1; 2; . . . ; n
ð3Þ
j¼1
P P
j 1
and to obtain ½ ni¼1 m
j¼1 M gi ;we perform the fuzzy addition operj
ation of Mgi ðj ¼ 1; 2; . . . ; mÞ values such that
n X
m
X
i¼1
Mjgi
n
X
¼
j¼1
li ;
j¼1
n
X
mi ;
j¼1
n
X
!
ui
ð4Þ
j¼1
And, the inverse of the vector is computed as
"
n X
m
X
i¼1
#1
0
¼@
Mjgi
j¼1
n
X
!1
ui
;
i¼1
n
X
!1
mi
;
i¼1
n
X
!1 1
A
li
ð5Þ
i¼1
(2) The degree of possibility of possibility of M2 ¼ ðl2 ; m2 ; u2 Þ P
M1 ¼ ðl1 ; m1 ; u1 Þ is defined as
VðM 2 P M1 Þ ¼ sup½minðM 1 ðxÞ; M 2 ðyÞÞ
ð6Þ
yPx
which can be equivalently express as
VðM 2 P M1 Þ ¼ hgtðM1 \ M2 Þ ¼ M 2 ðdÞ
ð7Þ
where d is the ordinate of the highest intersection point D between
uM1 and uM2 , shown as Fig. 2.
And, the ordinate of D is given by
VðM 2 P M1 Þ ¼ hgtðM1 \ M2 Þ ¼ M 2 ðdÞ
¼ ðl1 u2 Þ=ðm2 u2 Þ ðm1 l1 Þ
ð8Þ
To compare uM1 and uM2 ; we should need both the values of
VðM1 P M2 Þ and VðM 2 P M1 Þ.
(3) The degree of possibility for a convex fuzzy number to be
greater than k convex fuzzy numbers M i ði ¼ 1; 2; . . . ; kÞ could be defined as
VðM P M 1 ; M2 ; . . . ; Mk Þ ¼ V½ðM P M1 Þ and ðM
P M 2 Þ and and ðM P M k Þ ¼ min VðM P M i Þ;
i ¼ 1; 2; . . . ; k:
ð9Þ
Assume that
d’ðAi Þ ¼ min VðSi P Sk Þ
ð10Þ
for k = 1, 2, . . . , n; k – i. Then the weight vector is given by
Table 2
Membership function of linguistic scale
Fuzzy
number
Linguistic scale
~
1
~
2
~
3
~
4
~
5
Equally important
Intermediate
Weakly important
Intermediate
Essentially
important
Intermediate
Very strongly
important
Intermediate
Absolutely
important
~
6
~
7
~
8
~
9
Triangular fuzzy
scale
Triangular fuzzy
reciprocal scale
(1,
(1,
(2,
(3,
(4,
(1, 1, 1)
(1/3, 1/2,
(1/4, 1/3,
(1/5, 1/4,
(1/6, 1/5,
1,
2,
3,
4,
5,
1)
3)
4)
5)
6)
M2
M1
1
1)
1/2)
1/3)
1/4)
(5, 6, 7)
(6, 7, 8)
(1/7, 1/6, 1/5)
(1/8, 1/7, 1/6)
(7, 8, 9)
(9, 9, 9)
(1/9, 1/8, 1/7)
(1/9, 1/9, 1/9)
V (M2 ≥ M1)
0
D
l2
m2
l1
d
u2
m1
Fig. 2. The intersection between M1 and M2.
u1
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C.-T. Lin et al. / Expert Systems with Applications 36 (2009) 5613–5619
W’ ¼ ðd’ðA1 Þ; d’ðA2 Þ; . . . ; d’ðAn ÞÞT
ð11Þ
where Ai ði ¼ 1; 2; . . . ; nÞ are n elements.(4) Via normalization, the
normalized weight vectors are
W ¼ ðdðA1 Þ; dðA2 Þ; . . . ; dðAnÞÞT
ð12Þ
where W is a nonfuzzy number.
3.2. Analytic network process (ANP)
The ANP is the general form of the AHP, used in multi-criteria
decision-making to release hierarchical structure restriction
(Huang, Tzeng, & Ong, 2005). Whereas AHP denotes a framework
with a unidirectional hierarchical relationship, ANP permits more
complex interrelationships among decision levels and attributes.
The ANP feedback approach replaces hierarchies with networks,
in which the relationships between levels are not easily classified
simply as hierarchical versus non-hierarchical, or direct versus
indirect (Meade & Sarkis, 1999). For instance, not only does criteria
importance indicate importance as in a hierarchy, but alternative
importance may affect criteria importance (Saaty, 1996). Therefore, the conventional hierarchy framework cannot demonstrate
the complex system comprehensively.
The process to solve the ANP decision-making model is as
follows.
(1) Construct the hierarchy and interdependence of model.
A decision problem conceptual model should be developed
prior to conducting data collection. A hierarchy is a particular type
of system, based on the assumption that entities can be grouped
into disjoint sets, with the entities of one group influencing the
entities of other groups (Saaty, 1980). This is the most important
part in the qualitative component of ANP as Fig. 3 driving all criteria for the overall goal. The emphasis in the current paper uses the
ANP eigenvalue approach, an AHP extension and special interest
for comparative analysis.
The current study demonstrates the interdependent relationship among marketing resources criteria as illustrated in Fig. 3.
From a managerial capabilities prospect, the relationship is affected by market innovation capabilities and human resources assets, but not by customer linking capabilities and reputational
assets. For customer linking capabilities, human resource assets,
and reputation, there exists interdependent relationship among
each marketing resources. And, the relationship from market innovation capabilities is only not affected by reputational assets.
Goal
Selecting Marketing Strategy
MC
Criteria
CLC
RA
(2) Determine the pairwise comparisons and compute criteria
weights.
A ‘‘decision-making group” is the best source for ANP sample
data, because ANP is a main decision-making method in organizations. Relative important values in ANP are determined similar to
AHP using pairwise comparisons (Karsak, Sozer, & Alptekin,
2002), especially evaluations, allowing dependencies both within
inner dependence and outer dependence (Saaty, 1996). Pairwise
comparisons are carried out by one of the experts for a single decision maker for each evaluation framework node. Each rated score
in the questionnaire corresponds to each matrix of criteria. Each
pairwise comparison ratting is based on Saaty’s nine-point priority
scale. Additionally, numerical techniques are used to drive quantitative values from verbal comparisons.
The troubling ANP problem is to provide impartial and consistent comparison values for pairwise comparisons. Also, no two experts will make the same decision by pairwise comparison. So, in
order to assign weights to the evaluation criteria, there is need
for a broad expert poll as a common evaluation framework consensus. The questionnaire is created in accordance with associated
evaluation framework criteria. Consequently, four pairwise comparison matrices are obtained for the model elements, and each
performing the pairwise comparison process. The geometric mean
of all evaluations is also used to obtain the required pairwise comparison matrix.
(3) Build and solve the supermatrix.
Saaty (1996) states that the feedback approach, a hierarchy generalization, is used to derive system priorities with interdependent
influences. Saaty also points out that an implemented ANP model
follows three steps. All element interactions should be evaluated
by pairwise comparisons so as to construct the problem framework. In addition, a supermatrix, a matrix of influences among
the elements, should be obtained by these priority vectors. The
supermatrix derives from limiting powers to calculate the overall
priorities, so that the cumulative influence of each element on
every other element it interacts is obtained (Saaty & Vargas,
1998). The generalized supermatrix of the hierarchy with three
levels used in this paper is as follows:
Goal ðGÞ
W¼
Criteria ðCÞ
Alternatives ðAÞ
lim W k
Alternatives
HRA
Marketing Strategy Alternatives
Fig. 3. The evaluation framework for selecting marketing strategy.
ð13Þ
where w21 is a vector that represents the goal impact on criteria,
w32 is a matrix that represents the criteria impact on each alternative, w22 and w33 are identity matrices that represent criteria inner
dependence and the inner dependence of alternative. W is a
partitioned matrix because its entries are composed of the vectors
obtained from the pairwise comparisons. Since W is a column stochastic matrix, its limiting priorities depend on the reducibility
and cyclicity of that matrix. If the matrix is irreducible and
primitive, the limiting value is obtained by raising W to powers
such as Eq. (14) to get the global priority vectors (Saaty & Vargas,
1998)
k!1
MIC
G C A
3
0
0
0
6
7
4 w21 w22 0 5
0
w32 I
2
ð14Þ
(4) Select the best alternative.
Finally, after the supermatrix is assured of column stochastic, it
is raised to a sufficient large power until convergence occurs
(Saaty, 1996). That is, the supermatrix is then raised to limiting
powers to be W2k+1, where k is an arbitrarily large number capturing all interactions and obtains a steady-state outcome. Then, the
alternative with the highest overall priority should then be
selected.
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C.-T. Lin et al. / Expert Systems with Applications 36 (2009) 5613–5619
Table 4
The fuzzy evaluation of the alternatives with respect to each criterion
4. Numerical application for private hotels
Marketing experts desire to determine the appropriate marketing strategy to capture sustained competitive advantage. The current study mainly builds the marketing expert decision-making
process for marketing strategists to select the best marketing strategy. The application based on practical experience and implementation in private hotel is presented in this study to illustrate the
proposed marketing expert decision process. This study applies
the proposed process to solve the problem and demonstrates the
application.
Step 1. Construct the hierarchy and interdependent relationship
among marketing resources for marketing strategy.
After reviewing related literatures and interviewing private hotels practitioners and experts, three marketing strategies remain
for further evaluation. A decision-making group of twenty-three
evaluators, including practitioners and experts, are formed to
determine the most appropriate marketing strategy. The evaluation criteria include managerial capabilities, customer linking
capabilities, market innovation capabilities, human resource assets, and reputational assets. The interdependent relationship
among marketing resources is also elaborated by committee evaluators as illustrated in Fig. 3.
Step 2. Determine the pairwise comparisons with respect to marketing resources and marketing strategy.
Then, due to five criteria and three alternatives for the overall
goal, a total of eleven pairwise matrices totaling sixty-four pairwise comparisons were made. Only eight evaluators conduct interdependence matrices with respect to marketing resources. The
geometric mean is an appropriate rule for combining individual
judgments to obtain group judgment for matrices entries for integrating answers coming from the decision-making group. The decision-making group now compares marketing resources criteria of
with respect to goal shown as Table 3.
Step 3. Compute the criteria weights and interdependent weights of
marketing resources.
According to decision-making group answers presented in Table
3, the normalized weight vector is calculated as wGoal = (0.08, 0.40,
0.27, 0, 0.26)T. This work concludes that customer linking capabilities, market innovation capabilities, and reputational assets are
more important than managerial capabilities and human resources
assets. Moreover, this work also observes that customer linking
capabilities is more important than growth. Consequently, due to
small service operation size, the human resource assets is the least
important for private hotels.
With respect to marketing resources, the decision-making
group compares the marketing strategy alternatives. Given five
Table 3
The fuzzy evaluation matrix with respect to the goal
Marketing resources
MC
CLC
MIC
HRA
RA
MC
(1, 1, 1)
CLC
(1.30,
1.89)
(0.98,
1.52)
(0.52,
0.81)
(1.25,
1.96)
(0.53, 0.64,
0.77)
(1, 1, 1)
(0.66, 0.83,
1.03)
(1.02, 1.26,
1.52)
(1, 1, 1)
(1.24, 1.57,
1.93)
(1.93, 2.53,
3.15)
(1.57, 1.98,
2.45)
(1, 1, 1)
(0.59, 0.73,
0.91)
(1.04, 1.33,
1.69)
(0.88, 1.11,
1.39)
(0.49, 0.60,
0.74)
(1, 1, 1)
MIC
HRA
RA
1.57,
1.21,
0.64,
1.57,
(0.66, 0.80,
0.98)
(0.32, 0.40,
0.52)
(0.59, 0.75,
0.96)
(0.41, 0.51,
0.64)
(0.72, 0.90,
1.13)
(1.35, 1.67,
2.07)
Note: MC: managerial capabilities, CLC: customer linking capabilities, MIC: market
innovation capabilities, HRA: human resource assets, RA: reputational assets.
Marketing strategies
DS
Alternatives
DS
SS
CLS
Alternatives
DS
SS
CLS
Alternatives
DS
SS
CLS
Alternatives
DS
SS
CLS
Alternatives
DS
SS
CLS
with respect to MC
(1, 1, 1)
(0.73, 0.84, 0.99)
(0.60, 0.74, 0.95)
with respect to CLC
(1, 1, 1)
(0.53, 0.65, 0.81)
(0.41, 0.50, 0.63)
with respect to MIC
(1, 1, 1)
(0.48, 0.56, 0.68)
(0.56, 0.69, 0.89)
with respect to HRA
(1, 1, 1)
(0.55, 0.61, 0.71)
(0.65, 0.77, 0.93)
with respect to RA
(1, 1, 1)
(0.48, 0.55, 0.63)
(0.40, 0.48, 0.59)
SS
CLS
(1.01, 1.19, 1.37)
(1, 1, 1)
(0.76, 0.89, 1.05)
(1.06, 1.34, 1.67)
(0.95, 1.13, 1.32)
(1, 1, 1)
(1.24, 1.55, 1.90)
(1, 1, 1)
(0.70, 0.81, 0.94)
(1.75, 2.00, 2.43)
(1.06, 1.23, 1.43)
(1, 1, 1)
(1.38, 1.70, 2.022)
(1, 1, 1)
(0.78, 0.91, 1.07)
(1.12, 1.45, 1.79)
(0.94, 1.10, 1.28)
(1, 1, 1)
(1.42, 1.64, 1.81)
(1, 1, 1)
(1.01, 1.11, 1.23)
(1.08, 1.30, 1.53)
(0.82, 0.90, 0.99)
(1, 1, 1)
(1.58, 1.83, 2.07)
(1, 1, 1)
(0.65, 0.77, 0.95)
(1.69, 2.08, 2.49)
(1.06, 1.30, 2.49)
(1, 1, 1)
Note: DS: differentiation strategy, SS: segmentation strategy, CLS: cost leadership
strategy.
fuzzy comparison data of the alternatives for marketing resources
in Table 4, the normalized weight vector is calculated as
wMC ¼ ð0:42; 0:27; 0:31ÞT . This work also calculates the normalized
weight vectors of each criterion as wCLC ¼ ð0:70; 0:06; 0:25ÞT ,
wMIC ¼ ð0:74; 0:14; 0:12ÞT , wHRA ¼ ð0:80; 0; 0:20ÞT , and wRA ¼ ð1; 0;
0ÞT , respectively.
The decision-making group compares the interdependent relationship among marketing resources. Table 5 gives five fuzzy comparison data of interdependence matrices for marketing resources,
respectively. The decision-making group compares the interdependent relationship of marketing resources with respect to managerial capabilities. Thus, the normalized weight vector is calculated
T
as wANP
MC ¼ ð0:71; 0:20; 0:09Þ using Table 5 data. Also, the other four
matrices relevant to pairwise comparisons of interdependence
matrices are calculated. Accordingly, the weight vectors of each
T
ANP
matrix are calculated as wANP
CLC ¼ ð0:58; 0:03; 0; 0; 0:39Þ , wMIC ¼ ð0;
T
ANP
¼
ð0;
0:11;
0:39;
0:05;
0:41Þ
,
and
w
0:03; 0:97; 0ÞT , wANP
HRA
RA ¼ ð0:55;
0:14; 0:15; 0:16; 0ÞT .
Step 4. Build and solve the decision-making supermatrix.
In addition to the respective vectors and matrices previously
obtained, Table 6 presents the supermatrix. Since the supermatrix
includes interdependent relationship among marketing resources
criteria, not all of columns sum to one. A weighted supermatrix
is transformed first into a stochastic value. After entering the normalized values into the supermatrix and completing the column
stochastic, the supermatrix is then raised to a sufficiently large
power until convergence occurs. The current supermatrix reached
convergence and attained a unique eigenvector.
Step 5. Select the best marketing strategy.
Convergence in the current study is stable at W15 with cyclical
ratios, and the limit supermatrix, which shows long-term
stable values, is shown in Table 7. Overall priorities for marketing
strategy are given by the bottom left corner of W15. For the
decision problem goal, the alternative with the largest priority index should be selected. The differentiation strategy, with a relative
importance value of 0.69, is the best marketing strategy for selecting competitive marketing strategy, followed by cost leadership
strategy with a value of 0.17 and segmentation strategy with a
value of 0.12.
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C.-T. Lin et al. / Expert Systems with Applications 36 (2009) 5613–5619
Table 5
The fuzzy evaluation of interdependence with respect to marketing resources
Marketing resources
MC
CLC
The interdependence matrix with respect
MC
(1, 1, 1)
CLC
MIC
(0.58, 0.71, 0.91)
HRA
(0.31, 0.41, 0.61)
RA
The interdependence matrix with respect
MC
(1, 1, 1)
CLC
(0.31, 0.38, 0.47)
MIC
(0.39, 0.47, 0.61)
HRA
(0.167, 0.18, 0.22)
RA
(0.82, 1.03, 1.29)
The interdependence matrix with respect
MC
(1, 1, 1)
CLC
(0.70, 0.96, 1.27)
MIC
(1.64, 2.12, 2.58)
HRA
(0.45, 0.58, 0.74)
RA
The interdependence matrix with respect
MC
(1, 1, 1)
CLC
(1.81, 2.39, 2.90)
MIC
(1.92, 2.66, 3.48)
HRA
(1.12, 1.49, 2.00)
RA
(2.16, 2.69, 3.28)
The interdependence matrix with respect
MC
(1, 1, 1)
CLC
(0.35, 0.45, 0.58)
MIC
(0.43, 0.51, 0.64)
HRA
(0.43, 0.57, 0.74)
RA
(0.24, 0.30, 0.38)
MIC
HRA
RA
(1.10, 1.40, 1.74)
(1.64, 2.46, 3.20)
(1, 1, 1)
(0.74, 0.98, 1.22)
(0.82, 1.02, 1.35)
(1, 1, 1)
(2.12, 2.63, 3.20)
(1, 1, 1)
(0.50, 0.66, 0.91)
(0.31, 0.37, 0.45)
(1.74, 2.25, 2.90)
(1.64, 2.12,
(1.10, 1.53,
(1, 1, 1)
(0.45, 0.57,
(1.29, 1.66,
(4.57, 5.50,
(2.21, 2.73,
(1.37, 1.74,
(1, 1, 1)
(2.78, 3.56,
(0.79, 1.04, 1.43)
(1, 1, 1)
(2.00, 2.73, 3.54)
(0.35, 0.42, 0.55)
(0.39, 0.47, 0.61)
(0.28, 0.367, 0.50)
(1, 1, 1)
(0.18, 0.22, 0.28)
(1.35, 1.72, 2.21)
(1.812.36, 2.85)
(3.60, 4.60, 5.52)
(1, 1, 1)
(0.35, 0.42,
(1, 1, 1)
(1.55, 2.02,
(0.86, 1.08,
(1.92, 2.45,
(0.29, 0.38,
(0.40, 0.50,
(1, 1, 1)
(0.39, 0.46,
(0.79, 0.96,
0.52)
0.65)
(1.58, 1.94,
(0.58, 0.79,
(1, 1, 1)
(0.74, 0.94,
(0.45, 0.57,
2.34)
1.10)
to MC
to CLC
2.58)
2.00)
0.73)
2.12)
6.36)
3.20)
2.21)
(0.78, 0.98,
(0.35, 0.45,
(0.47, 0.60,
(0.23, 0.28,
(1, 1, 1)
1.22)
0.58)
0.77)
0.36)
(0.55, 0.76, 1.04)
(0.73, 0.939, 1.17)
(1.74, 2.17, 2.58)
(1, 1, 1)
(1.64, 2.05, 2.38)
(0.31, 0.37,
(0.33, 0.41,
(0.82, 1.04,
(0.42, 0.49,
(1, 1, 1)
0.46)
0.52)
1.27)
0.61)
(1.35, 1.76, 2.34)
(0.60, 0.80, 1.10)
(0.81, 1.06, 1.35)
(1, 1, 1)
(0.43, 0.51, 0.64)
(2.63, 3.39,
(2.00, 2.54,
(1.29, 1.74,
(1.58, 1.94,
(1, 1, 1)
4.24)
3.02)
2.25)
2.34)
4.42)
to MIC
to HRA
0.55)
2.48)
1.37)
3.02)
0.58)
1.22)
to RA
(1.74, 2.25,
(1, 1, 1)
(0.91, 1.26,
(0.91, 1.25,
(0.33, 0.39,
2.90)
1.74)
1.67)
0.50)
1.24)
0.77)
Note: MC: managerial capabilities, CLC: customer linking capabilities, MIC: market innovation capabilities, HRA: human resource assets, RA: reputational assets.
Table 6
The initial completed supermatrix, W
Goal
MC
CLC
MIC
HRA
RA
DS
SS
CLS
Goal
MC
CLC
MIC
HRA
RA
DS
SS
CLS
0.08
0.40
0.27
0
0.26
0.71
0.58
0.03
0
0
0.39
0.70
0.06
0.25
0
0.03
0.97
0
0
0.11
0.39
0.05
0.41
0.80
0
0.20
0.55
0.14
0.15
0.16
0
1
0
0
1
0
0
0
1
0
0
0
1
0.20
0.09
0.42
0.27
0.31
0.74
0.14
0.12
Table 7
The limited completed supermatrix, W15
Goal
MC
CLC
MIC
HRA
RA
DS
SS
CLS
Goal
MC
CLC
MIC
HRA
RA
DS
SS
CLS
0
0
0
0
0
0
0.69
0.12
0.17
0
0
0
0
0
0
0.49
0.24
0.27
0
0
0
0
0
0
0.64
0.12
0.23
0
0
0
0
0
0
0.74
0.14
0.12
0
0
0
0
0
0
0.76
0.05
0.17
0
0
0
0
0
0
0.80
0.09
0.11
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
5. Conclusions
Marketing strategy decision-making is at the core of managerial
implementation for service industry. The current paper proposed a
systematic and flexible marketing expert decision process to deter-
mine appropriate marketing strategy efficiently and effectively.
Thus, this process enables marketing experts or decision makers
to identify optimum marketing strategy. Significantly, this study
provides procurement personnel with an easily applied and objective method of assessing marketing strategy appropriateness.
The contribution of the current study lies in practical implementation for a fuzzy ANP approach for the proposed decisionmaking process to be utilized by marketing experts in a real industry to determine marketing strategy appropriateness. The ANP is
presented in this study as a valuable method to support efficient
marketing strategy selection for marketing experts. A review of relative literatures and practical experience consideration, results in
the marketing expert decision process consisting of the following
steps: (1) construct the hierarchy and interdependence of model;
(2) determine the pairwise comparisons; (3) compute the weights
of criteria; (4) build and solve the supermatrix; (5) select the best
marketing strategy alternative. These five reasonable and understandable steps aid marketing experts in making decision under
a complex environment.
The ANP concept has evolved to deal with interdependent relationships among marketing resources criteria. This study is probably the first attempt to apply fuzzy ANP in the marketing strategy
decision-making process. This study demonstrates an example to
illustrate the steps of fuzzy ANP in marketing strategy. ANP, which
considers the interdependent relationship among criteria, should
be adopted if possible. The results of stable system testing provide
guidance for a multi-criteria environment in accepting ranks when
its criteria consider interdependent relationships. This study finds
that fuzzy ANP is a promising methodology for evaluating appropriate marketing strategy alternatives.
The current work successfully applied the fuzzy ANP to the case
described here. Consequently, the fuzzy ANP improves upon the
popular MCDM approach to alternative prioritization for this case.
C.-T. Lin et al. / Expert Systems with Applications 36 (2009) 5613–5619
An appropriate and simple prioritization method for determining
the best marketing strategy would be helpful to practitioners and
marketing experts. The systematic decision-making process for
marketing strategy determination in practical implementation
could be easily extended to the decision-making for other managerial problems. Furthermore, optimizing a decision-making process
in a practical and complex managerial environment could be considered critical for future marketing strategy.
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