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Expert Systems with Applications 36 (2009) 5613–5619 Contents lists available at ScienceDirect 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 5614 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). 5615 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 5616 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. 5617 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. 5618 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. 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