MODELLING IMPORTANCE PREFERENCES IN CUSTOMER SATISFACTION SURVEYS
lượt xem 22
download
MODELLING IMPORTANCE PREFERENCES IN CUSTOMER SATISFACTION SURVEYS
Customer satisfaction measurement, through MUSA model, provides the analysts with the highest and lowest performance indicators, pointing out the leverage opportunities and the weaknesses of the company. An extension of the MUSA methodology for modelling customer importance preferences for service characteristics is presented in this paper. Several approaches in the context of multiobjective linear programming are examined, which give the ability to compare derived and modelled weights of the satisfaction dimensions and to introduce the principles of Kano’s model to MUSA methodology. Finally, the results of an application of the MUSA extension to an educational organization are presented in this......
Bình luận(0) Đăng nhập để gửi bình luận!
Nội dung Text: MODELLING IMPORTANCE PREFERENCES IN CUSTOMER SATISFACTION SURVEYS
 MODELLING IMPORTANCE PREFERENCES IN CUSTOMER SATISFACTION SURVEYS E. Grigoroudis(1), Y.Politis(1), O. Spyridaki(1) and Y. Siskos(2), (1) Technical University of Crete Decision Support Systems Laboratory University Campus, 73100 Chania, Greece Tel. +30821037346 / Fax +30821064824 Email: vangelis@ergasya.tuc.gr (2) University of Piraeus Department of Informatics Karaoli Dimitriou 80, 18534 Piraeus, Greece Tel. +30104142260 / Fax +30104142264 Email: ysiskos@unipi.gr ABSTRACT Customer satisfaction measurement, through MUSA model, provides the analysts with the highest and lowest performance indicators, pointing out the leverage opportunities and the weaknesses of the company. An extension of the MUSA methodology for modelling customer importance preferences for service characteristics is presented in this paper. Several approaches in the context of multiobjective linear programming are examined, which give the ability to compare derived and modelled weights of the satisfaction dimensions and to introduce the principles of Kano’s model to MUSA methodology. Finally, the results of an application of the MUSA extension to an educational organization are presented in this paper. Key words: Customer satisfaction analysis, MUSA method, Satisfaction importance modelling, Kano’s model. 1. INTRODUCTION To reinforce customer orientation on a daytoday basis, a growing number of companies choose customer satisfaction as their main performance indicator. However, customer satisfaction must be translated into a number of measurable parameters directly linked to several aspects of a company’s products/services or else it will remain an abstract and intangible notion. Measurement will provide the analysts with the highest and lowest performance indicators, pointing out the leverage opportunities and the weaknesses of the company. It often happens that derived importance by a preference disaggregation model differs from the stated importance. By the term stated importance we refer to the importance that is given to each criterion by the customers. It is not unreasonable to say that customers tend to rate every criterion as important, when asked freely (Naumann and Giel, 1995). The aim of this paper is to present an extension of the MUSA methodology that helps modelling customer importance preferences for service characteristics. This approach gives the ability to compare derived and modelled weights of the satisfaction dimensions and to extrapolate valuable results. The results of an application of the MUSA extension to an educational organization, give an example of the differentiation between derived and stated importance. This paper is organised in 4 sections. Section 2 presents briefly the mathematical background of importance preferences modeling. Here are presented the basic principles of Kano’s model and 1
 MUSA method, as well as a summary description of customers’ preferences importance modelling with MUSA. The main results of the application for the customers of an educational organization are presented in section 3. Section 4 summarizes some concluding remarks, along with the basic advantages of the MUSA extension. 2. DIFFERENT METHODOLOGICAL APPROACHES FOR CUSTOMER SATISFACTION 2.1. Kano’s model for customer satisfaction In many cases customer satisfaction has been seen mostly as a onedimensional construction – the higher the perceived product quality, the higher the customer’s satisfaction and vice versa. But fulfilling the individual product/service requirements to a great extent does not necessarily imply a high level customer satisfaction. It is also the type of requirement which defines the perceived product/service quality and thus customer satisfaction. A characteristic example of this situation is the assessment of customer satisfaction for a pen point (Vavra, 1997). If the flow of the ink is not sufficient (or it is more than needed), customers will state a high level of dissatisfaction. On the other hand, if the flow of the ink is sufficient, it is possible that the customers will not state a high level of satisfaction, considering that the particular attribute is a necessary and expected feature of the product. In his model (see figure 1), Kano (1984) distinguishes between three types of product/service requirement which influence customer satisfaction in different ways when met. Based on the Kano model, it can be recognized that customer satisfaction is more than onelevel issue as traditionally viewed. It may not be enough to merely satisfy customers by meeting their basic and spoken requirements under current highly competitive environments. One main reason is that nowadays there are so many similar products for customers to choose from in the marketplace. The three types of product/service requirements in the Kano model are (Kano 1984): Mustbe requirements The must be requirements are basic criteria of a product/service. If these requirements are not fulfilled, the customer will be extremely dissatisfied. On the other hand, as the customer takes these requirements for granted, their fulfillment will not increase his satisfaction. Fulfilling the mustbe requirements will only lead to a state of “not dissatisfied”. The customer regards the mustbe requirements as prerequisites, he takes them for granted and therefore does not explicitly demand them. Mustbe requirements are in any case a decisive competitive factor, and if they are not fulfilled, the customer will not be interested in the product/service at all. One dimensional requirements With regard to these requirements, customer satisfaction is proportional to the level of fulfillment – the higher the level of fulfillment, the higher the customer’s satisfaction and vice versa. Onedimensional requirements are usually explicitly demanded by the customer. Attractive requirements These requirements are the product/service criteria which have the greatest influence on how satisfied a customer will be with a given product/service. Attractive requirements are neither explicitly expressed nor expected by the customer. Fulfilling these requirements leads to more than proportional satisfaction. If they are not met, however, there is no felling of dissatisfaction. It must be noticed that the specific classification of customer requirements to one of the above categories is dynamic and affected from the competitiveness of the market. Thereby, an attractive attribute of a product/service may in a short time become onedimensional or even expected attribute. 2
 Customer satisfied Attractive requirements  not expressed  customertailored One dimensional  cause delight requirements  articulated  specified  measurable  technical Requirement Requirement not fulfilled fulfilled Mustbe requirements  implied  selfevident  not expressed  obvious Customer dissatisfied Source: Berger et al., 1993 Figure 1: Kano’s model of customer satisfaction The advantages of classifying customer requirements by means of the Kano method are very clear (Matzler et al., 1996, Matzler and Hinterhuber, 1998): • Product requirements are better understood: the product/service criteria which have the greatest influence on the customer’s satisfaction can be identified. Classifying product/service requirements into mustbe, one dimensional and attractive dimensions can be used to focus on. • Priorities for product development. It is, for example, not very useful to invest in improving mustbe requirements which are already at a satisfactory level but better to improve one dimensional or attractive requirements as they have a greater influence on perceived product/service quality and consequently on the customer’s level of satisfaction. • Kano’s method provides valuable help in tradeoff situations in the product development stage. If two product requirements cannot be met simultaneously due to technical or financial reasons, the criterion which has the greatest influence on customer satisfaction can be identified. • Mustbe, onedimensional and attractive requirements differ, as a rule, in the utility expectations of different customer segments. From this starting point, customertailored solutions for special problems can be elaborated which guarantee an optimal level of satisfaction in the different customer segments. • Discovering and fulfilling attractive requirements creates a wide range of possibilities for differentiation. A product which merely satisfies the mustbe and onedimensional 3
 requirements is perceived as average and therefore interchangeable (Hinterhuber et al., 1994). • Kano’s model of customer satisfaction can be optimally combined with quality function deployment. A prerequisite is identifying customer needs, their hierarchy and priorities (Griffin and Hauser, 1993). Kano’s model is used to establish the importance of individual product/service features for the customer’s satisfaction and thus it creates the optimal prerequisite for processoriented product development activities. 2.2 Satisfaction and customer loyalty There have been extensive studies about the linkage between satisfaction and customer loyalty. As many researchers suggest, customer loyalty is a combination of both behaviours and attitudes (Dick and Basu, 1994; Oliver 1996; Allen and Rao, 2000; Jacoby, 1978). This means that loyal customers are those who have a favourable attitude and repeated purchases as well. Oliver (1996) defines loyalty as a strong commitment of customers that will repeat the purchase or will continue to be customers of a product or a service in the future, no matter what the impact of various situations or the efforts of marketing that aims to the change of customers’ purchase behaviour are. In most cases, customer satisfaction is a necessary but not sufficient condition for loyalty. Satisfaction is directed specifically at product/service characteristics, and may be relatively more dynamic measure. In contrast, customer loyalty is a broaden, more static attitude toward a company in general, and it may include both rational and emotional elements. In any case, it is generally accepted that loyalty is affected by customer satisfaction in direct or indirect way (Vavra, 1997; Oliver 1996; Allen and Rao, 2000). There are several types of loyalty according to the market conditions or the customer attachment toward a product/service. Furthermore, different levels of customer loyalty exist in relation to the degree of positive commitment (Hill, 1996). The most common acceptable measures of loyalty are customer retention (repurchase intention) and willingness to recommend the product/service to other consumers. Of much interest is the work of Oliva et al. (1992, 1995) were there is an attempt to study and analyse the correlation of customer loyalty with customer satisfaction, by using the basic principles of catastrophe theory. 3. THE MUSA METHOD (Grigoroudis and Siskos, 2002) The MUSA model is based on the principles of multicriteria analysis, using ordinal regression techniques. The main objective of the MUSA method is the aggregation of individual judgments into a collective value function via a linear programming disaggregation formulation. The assumption is made that client’s global satisfaction depends on a set of criteria or variables representing service characteristic dimensions. According to the model, each customer is asked to express his/her preferences, namely his/her global satisfaction and his/her satisfaction with regard to the set of discrete criteria. MUSA assesses global and partial satisfaction functions Υ* and Χi* respectively, given customers’ judgments Υ and Χi. The method follows the principles of ordinal regression analysis under constraints using linear programming techniques (JacquetLagrèze and Siskos, 1982; Siskos and Yannacopoulos, 1985; Siskos, 1985). The ordinal regression analysis equation has the following form (Table 1 presents model variables): 4
 * n Y = ∑ bi X i* i =1 n (1) b =1 ∑ i i =1 where the value functions Y * and X i* are normalised in the interval [0, 100], and bi is the weight of the ith criterion. Table 1: Variables of the MUSA method Y : client’s global satisfaction α : number of global satisfaction levels m y : the mth global satisfaction level (m=1, 2, ..., α) n : number of criteria Xi : client’s satisfaction according to the ith criterion (i=1, 2, …, n) αi : number of satisfaction levels for the ith criterion k xi : the kth satisfaction level of the ith criterion (k=1, 2, ..., αi) Y* : value function of Y y*m : value of the ym satisfaction level X*i : value function of Xi xi*k : value of the xik satisfaction level The normalisation constraints can be written as follows: y*1 = 0 , y*α = 100 *1 (2) xi = 0 , xi = 100 for i=1,2 ,… ,n *α i Furthermore, because of the ordinal nature of Y and X i the following preference conditions are assumed: y* m ≤ y* m +1 ⇔ y m ≺ y m +1 for m = 1,2 ,… ,α − 1 *k (3) xi ≤ xi* k +1 ⇔ xik ≺ xik +1 for k = 1,2 ,… ,αi − 1 where ≺ means “less preferred or indifferent to”. The MUSA method infers an additive collective value function Υ * , and a set of partial satisfaction functions Χ i* from customers’ judgements. The main objective of the method is to achieve the maximum consistency between the value function Υ * and the customers’ judgements Υ . Based on the modelling presented in the previous section, and introducing a doubleerror variable, the ordinal regression equation becomes as follows: 5
 n ~ Y * = ∑ bi Χ i* − σ + + σ − (4) i =1 ~ where Y * is the estimation of the global value function Y * , and σ + and σ − are the overestimation and the underestimation error, respectively. Equation (4) holds for customer who has expressed a set of satisfaction judgements. For this reason a pair of error variables should be assessed for each customer separately (Figure 2). Y* 100 ... σj+ y*m σj ... y*2 Y 0 y1 y2 ... ym ... yα Figure 2: Error variables for the jth customer Removing the monotonicity constraints, the size of the previous LP can be reduced in order to decrease the computational effort required for optimal solution search. This is effectuated via the introduction of a set of transformation variables, which represent the successive steps of the value functions Υ * and Χ i* (Siskos and Yannacopoulos, 1985; Siskos, 1985). The transformation equation can be written as follows (see also Figure 3): zm = y * m +1 − y* m for m=1,2 ,...,α − 1 (5) wik = bi xi − bi xi for k=1,2,...,αi − 1 and i=1,2,...,n * k +1 *k It is very important to mention that using these variables, the linearity of the method is achieved since equation (4) presents a nonlinear model (the variables Υ * and Χ i* , as well as the coefficients bi should be estimated). 6
 Y* Xi* 100 100 zα1 wiα i −1 ... ... bi y*m xi*k ... ... z2 wi 2 y*2 bi z1 xi*2 wi1 Y bi Xi 0 0 y1 y2 ... ym ... yα xi1 xi2 ... xik ... xiαi Figure 3: Transformation variables zm and wik in global and partial value functions According to the aforementioned definitions and assumptions, the basic estimation model can be written in a linear program formulation as it follows: M [min]F = ∑σ j =1 + j +σ − j under the constraints n t ji −1 t j −1 ∑ ∑ w −∑ z i =1 k =1 ik m =1 m − σ + + σ − = 0 , for j = 1,2,..., M j j a −1 ∑z m =1 m = 100 (6) n ai −1 ∑∑ w i =1 k =1 ik = 100 z m ≥ 0 , wik ≥ 0 , ∀m, i, k σ + ≥ 0 , σ − ≥ 0 , for j = 1,2,..., M j j where M is the number of customers. The preference disaggregation methodology consists also of a post optimality analysis stage in order to face the problem of multiple or near optimal solutions. The MUSA method applies a heuristic method for near optimal solutions search (Siskos, 1984). The final solution is obtained by exploring the polyhedron of near optimal solutions, which is generated by the constraints of the above linear program. During the post optimality analysis stage of the MUSA method, n linear programs (equal to the number of criteria) are formulated and solved. Each linear program maximizes the weight of a criterion and has the following form: ai −1 [max]F ′ = ∑ wik , for i = 1,2,..., n k =1 under the constraints 7
 F ≤ F* +ε (7) all the constraints of LP (6) where ε is a small percentage of F*. The average of the optimal solutions given by the n LPs (7) may be considered as the final solution of the problem. The model provides collective global and partial satisfaction functions as well as average satisfaction indices and weights that represent the relative importance of each criterion/subcriterion. 4. MODELLING PREFERENCES FOR CRITERIA IMPORTANCE In order to model customers’ preferences, customers are asked, via a specialized questionnaire, to place each one of the satisfaction criteria in one of the following categories: C1 = very important criterion, C2 = important criterion, C3 = less important criterion. Considering that C1, C2, C3 are ordered in a 0 to 100% scale, there are two preference thresholds T1 and T2, which define the % rate, which distinguishes each one of the three categories (see Figure 4). C3 C2 C1 0% T2 T1 100% Figure 4: Clauses of customers’ importance preferences 4.1 Weight estimation using ordinal regression techniques The main purpose of this approach is the comparative analysis between the derived importance of the criteria through the MUSA method and the stated importance given by the customers. In order to estimate the stated importance of the criteria, which is a qualitative variable, a linear program is formulated. The program calculates the two preference thresholds T1, above which a criterion is considered very important, and T2, below which a criterion is considered less important. In this way, the importance of each criterion according to customers’ preferences can be assessed and compared with the results of the MUSA method. For each criterion i =1,2,..n and each customer j = 1,2,..., M (where M is the number of customers and n is the number of criteria) we set the following constraints: • ˆ If bij ∈ C1, that is customer j considers criterion i ‘very important’ then: a −1 i ∑w t =1 it 100 Τ1 + Sij+ ≥ 0 • ˆ If bij ∈ C2, that is customer j considers criterion i ‘important’ then: a −1 i ∑w t =1 it 100 Τ1  Sij ≤ 0 8
 a −1 i ∑w t =1 it 100 Τ2 + Sij+ ≥ 0 • ˆ If bij ∈ C3, that is customer j considers criterion i ‘less important’ then: a −1 i ∑w t =1 it 100 Τ2  Sij ≤ 0 where Sij+ and Sij are the overestimation and underestimation error, respectively, for the ith criterion of the jth customer, C1, C2, C3 are the customers’ preference categories, T1 and T2 are the preference thresholds, αi is the number of satisfaction scale levels for i criterion, and wit is a MUSA variable. The final linear program is: [min] ∑∑ S j i + ij − + S ij under the constraints ai −1 ∑w t =1 it + ˆ − 100T1 + S ij ≥ 0, bij ∈ C1 ai −1 ∑w it − − 100T1 − S ij ≤ 0 t =1 ˆ bij ∈ C 2 ∀ i = 1,2,..., n and j = 1,2,..., M (8) ai −1 ∑w t =1 it − 100T2 + S ≥ 0 + ij ai −1 ∑w t =1 it − ˆ − 100T2 − S ij ≤ 0, bij ∈ C 3 n ai −1 ∑∑ w i =1 k =1 ik = 100 T2 ≥ λ T1 − T2 ≥ λ After the solution of LP (8) a post optimality analysis follows, where n linear programs (equal to the number of criteria), are formed and solved. Those linear programs maximize the weights bi of the criteria and have the following form: ai −1 [max]F ′ = ∑ wik , for i = 1,2,..., n k =1 under the constraints F ≤ F* +ε (9) all the constraints of LP (8) 9
 where F*is the optimal solution of the objective function of LP (8) and ε is a small percentage of F*. 4.2 Extension of the MUSA model The main purpose of this analysis is to examine whether additional information about the weights of the criteria can improve the results of the MUSA method. The examination of possible improvement is done through the Average Stability Index (ASI). ASI is the mean value of the normalized standard deviation of the estimated weights bi and is calculated as follows: 2 n ( ) n n∑ bi − ∑ bi j j 2 1 n j =1 ASI = 1 − ∑ j =1 , where bi j is the estimated weight of the criterion i, in n i =1 100 n − 1 the jth postoptimality analysis LP (Grigoroudis and Siskos, 2002). At first, it is examined the following Mulltiobjective Linear Programming (MOLP) problem: M [min] F1 = ∑σ j =1 + j +σ − j [min] F2 = ∑∑ S j i + ij − + S ij under the constraints all the constraints of LP (6) all the constraints of LP (8) (10) In a Mulltiobjective problem it is pointless to try to find out a solution which will optimize all the criteria of the objective functions simultaneously, considering that, in most of the cases, the criteria are competitive, that is the optimal value of one criterion is not optimal for the other. A basic tool for the representation of the competitiveness among multiple objective functions is the payoff matrix. This table represents the values that the multiple objective functions take when optimizing the value of one of these objective function. This multiobjective problem could be solved according to any MOLP method. Here the following heuristic method is chosen: Stage A: Solution of the following linear program M min] F1 = ∑σ j =1 + j +σ − j under the constraints all the constraints of LP (6) all the constraints of LP (8) (11) Stage B: Minimize Sij+ and Sij errors through LP (12): 10
 [min] F2 = ∑∑ S j i + ij − + S ij under the constraints F1 ≤ F1 + ε 1 * all the constraints of LP (6) all the constraints of LP (8) (12) where F1* is the optimal solution of the objective function of LP (11) and ε is a small percentage of F1*. Stage C: At this postoptimality analysis stage n linear programs are formed and solved, one for each of the n satisfaction criteria. Those linear programs maximize the bi of each criterion: ai −1 [max]F ′ = ∑ wik , for i = 1,2,..., n k =1 under the constraints F1 ≤ F1 + ε 1 * F2 ≤ F2 + ε 2 * all the constraints of LP (6) all the constraints of LP (8) (13) where ε1 and ε2 are small and positive numbers, F1* and F2* are the optimal solutions of the objective functions of LP (11) and LP (12), respectively. 5. ANALYSIS OF THE RESULTS 5.1 Dual Importance Window In order to examine the relation between the stated and derived importance, a diagram, which combines the derived importance of the criteria, calculated by the MUSA method, and the stated importance, given by the customers, is created (Figure 5). In quadrants II and I appear the dimensions that are truly important to the customers. Those are the main characteristics that management and production should focus on. In quadrants IV and I appear the important dimensions according to the customers’ free statement. Those are the dimensions that marketing should focus on. When a characteristic appears in quadrant III or I there is an agreement between derived and stated importance. That is both MUSA and the customers consider a characteristic of quadrant I (III) of high (low) importance. On the other hand, in quadrants II or IV there is a disagreement between the stated and derived importance. This disagreement is an indication that those dimensions demand for further analysis. The diagram in figure 4 can also be interpreted as a ‘Dual Importance Window’ (Lowenstein, 1995). Such an approach agrees with the approach of Kano’s model and its three basic categories of product/service requirements. Quadrants III and I correspond to the characteristics that are truly important or truly unimportant for the customers (onedimensional characteristics). Both the model and the customers agree on them giving the company a more valid view. In 11
 quadrant II appear the characteristics that the model rates as very important, but the customers as less important, when they are asked straightforward. Those characteristics are called ‘unspoken motivators’ and represent dimensions to which the company should pay attention. They may affect the future clientele, positively or negatively, although the customers consider them of low importance. Finally in quadrant IV appear the characteristics that the model rates as less important, but the customers as very important. These usually include expected or costof entry services, such as the guarantee that a product is expected to give for its products. A company should keep such characteristics at a level at least as high as of the competitive companies in order to keep its clientele, or offer extra, unexpected services to gain competitive advantage. IV I High EXPECTED (Truly important) (Expected/cost of entry) Stated Importance L NA S IO EN IM ED ON II III ATTRACTIVE Low (Truly unimportant) (Unspoken motivator) Low High Derived Importance Source: ARBOR, Inc., 1991 Figure 5: Dual importance window 5.2 Customer Motivation Window (Lowenstain, 1995) In true mathematical terms, the Customer Motivation Window is a correlation or simple regression model, in which the score on each performance attribute (independent variable) is correlated with an overall performance measure, such as intended future purchase with an overall performance measure, such as intended future purchase or recommendation (dependent variable), to see how they align. The dependent variable most representative of motivation leading to action is purchase intent, or loyalty. Importance in the Customer Motivation Window is calculated by the degree of correlation between individual attribute ratings and the overall performance measure rating. This is done for each customer, and modeled importance is the relationship of the attributes with how well they correlate with the overall performance measurement ratings for all customers or a customer segment. Importance and priority is expressed by placement of attributes in quadrants on a graphic. Quadrants can be described as follows (figure 6): 12
 • Quadrant I – high attribute performance scores/high correlation with positive intended action (likelihood to remain loyal). These are attributes of high (probable) positive leverage for the company that they should continue to emphasize. • Quadrant II – high attribute performance scores/low correlation with positive intended action (likelihood to remain loyal). These attributes, while performed well, have relatively little leveraging impact on motivation for intended action. This may be a communication issue for the company or it may simply be one of those expected attributes that must be performed but not be improved. • Quadrant III – low attribute performance scores/low correlation with negative intended action (likelihood to remain loyal). These are the attributes that provide little value to the customer or the company. If possible, the company should downscale or even eliminate activities in these areas. Though not well performed, customers are relatively unlikely to miss them. • Quadrant IV  – low attribute performance scores/high correlation with negative intended action (likelihood to remain loyal). The low attribute performance ratings closely relate to low likelihood to remain loyal, so the company must target attributes in this quadrant for improvement. IV I High Must Improve Highest Leverage Modeled Importance II III Lowest priority for Low Less Important improvement Low High Quality Rating Source: ARBOR, Inc., 1991 Figure 6: Customer motivation window 6. AN APPLICATION TO AN EDUCATIONAL ORGANISATION The MUSA and the extension of MUSA methods were applied to an educational organization in order to assess the students’ preferences and the differences of the results produced from the two methods. The satisfaction criteria that were examined concern the provided services, the educational process, the secretarial support, the additional services and the image of the organization. The performed analysis concern: 13
 • Weights’ estimation through ordinal regression techniques – Its main objective is the comparative analysis between the derived importance of the criteria, calculated through the MUSA method, and the stated importance given by the customers. • Extension of the MUSA method – Its main objective is the examination of the possible improvements of the MUSA’s results with the introduction of additional information for the weights of the criteria. The examination of this possible improvement is based on the average stability index (ASI). 6.1 Results of the weight estimation model Different values of λ for the two constraints T2≥λ and T1T2≥λ of the linear program 8 (§ 4.1) 100 have been chosen. It is interesting to notice that these values should be λ ≤ % , where n is n the number of criteria. This is due to the maximum value which λ can take, considering that it cannot exceed the weight that the criteria would have if they were all of equal importance. In the current case study there are five criteria, therefore λ≤0.2. After many tests, the better results appeared for λ=0.15. The final results of the weight estimation through ordinal regression techniques, as well as the results of the MUSA model are presented in Table 2. According to the weight estimation model, all the criteria have almost the same weight. This is hardly a surprise, since the customers, when asked freely, have the tendency to rate everything as very important (Naumann and Giel, 1995). Table 2: Weight comparison between MUSA model and Weight estimation model Criteria Weight estimation model MUSA Provided services 22.752 33.300 Educational process 29.070 20.000 Secretarial support 16.467 20.000 Additional services 15.733 13.300 Image 15.977 13.300 The results of the two analyses were normalized and presented in a dual importance window (Figure 7). According to figure 5, there is an agreement between the stated and the derived importance for the criteria of the ‘Provided Services’, ‘Additional Services’ and ‘Image’. The first is considered to be of high importance while the other two of low importance in both cases. The criteria of ‘Educational Process’ and ‘Secretarial Support’ should be further examined since they appear in between quadrants IVI and IIIII respectively. The educational organization should focus the management efforts on ‘Provided Services’, ‘Educational Process’, and ‘Secretarial Support’ that are the truly important dimensions according to the MUSA model. Moreover, it should focus the marketing efforts, mainly, on the ‘Educational Process’ and the ‘Provided Services’. These are the two most important criteria according to the customers’ stated opinion. 14
 Importance High educational process provided services Stated image secretarial support additional services Low Low Derived High Figure 7: Dual importance window of the educational organization Interpreting Figure 7 as a ‘Dual Importance Window’, we can see that the criterion of ‘Provided Services’, which appears in quadrant I, and the criteria of ‘Image’ and ‘Additional Services’, which appear in quadrant III, are onedimensional attributes and represent the basic requirements and desires of customers. This means that an increase in the performance of these criteria will lead to a proportional increase of customer satisfaction. The criterion of ‘Educational Process’ lies between quadrant IV, where appear the attracted attributes, and quadrant I, where appear onedimensional (truly important) attributes. This means that it is quite possible that a high performance in this particular criterion will not necessary imply a high customer satisfaction index, while, in the contrary, a low performance can lead to high dissatisfaction. On the other hand, the criterion of ‘Educational Support’ lies between quadrant II, where appear the attractive attributes, and quadrant III, where appear onedimensional (truly unimportant) attributes. This means that it is quite possible that a high performance in this particular criterion will lead to high satisfaction, while in the contrary, a low performance will not necessary imply low dissatisfaction. 6.2 Results of the MUSA’s extension The results of the MOLP problem described in paragraph 4.2 are presented in Table 3. It is obvious that the two objective functions are highly competitive, as the minimization of each function causes a high increase of the other. Table 3: Payoff matrix F1 F2 [min] F1 0 3860 [min] F2 5160 90 After many tests for different values of λ, λ=0.1 has been chosen as the value that gave the best results. The results of the heuristic method described in paragraph 4.2 with F1*≤10 and F2*≤3036 are presented in table 4. Both MUSA and the extension of MUSA consider the criteria 15
 of ‘Provided Services’, ‘Educational Process’, and ‘Secretarial Support’ as the most important. The only difference appears in the order with which each criterion is considered to be important. While in the MUSA method the ‘Provided service’ criterion is considered as the most important criterion and the ‘Educational Process’ follows in significance, the opposite holds for the extension of MUSA. General speaking, both the MUSA method and the extension of MUSA give results that are very similar. However, the increase of ASI shows that the extra information included in the original MUSA method can improve its stability. As a conclusion, MUSA extension can sometimes improve the MUSA model. Table 4: Comparative results between MUSA extension and MUSA model Criteria MUSA’s extension MUSA Provided services 26.3 33.3 Educational process 39.1 20.0 Secretarial support 24.6 20.0 Additional services 8.0 13.3 Image 2.0 13.3 ASI 76.59% 72.55% 6.3 Analysis of customer loyalty A further analysis for the examination of the relationship that the different type of attributes (onedimensional, expected, attractive) has with customer loyalty is attempted in this section. Specifically, it is examined the correlation of the criteria with the intention of customers to reuse the services of the particular educational organization. Considering that the data, which were collected through a specialized questionnaire, are qualitative, the Spearman’s R and the Kendall’s tau were chosen as the most appropriate indicators (Table 5). Table 5: Correlation of criteria with the intention to reuse the services of the educational organization Spearman’s R Kendall’s tau Provided services 21% 19.9% Educational process 31% 29.1% Secretarial support 33.7% 31.6% Additional services 20.5% 18.6% Image 18.2% 16.5% According to Table 5, the criteria of ‘Educational Process’ and ‘Secretarial Support’ that tend to become expected and attractive attributes respectively, have the highest correlation with the reuse intention. On the other hand, the onedimensional characteristics seem to be of less importance with regard to the reuse intention. With respect to the customer motivation window of Figure 8, which combines the performance of each one of the criteria and their relationship with the reuse intention, it can be observed that the criteria of ‘Educational Process’ and ‘Secretarial Support’ are the attributes of high (probable) positive leverage for the company. These are the criteria that the organization should continue to emphasize. The ‘Additional Services’ and the ‘Image’ criteria, which are the truly unimportant criteria according to figure 7, are the ones that provide little value to the customers of the organization and consider to be of less importance. Finally, the ‘Provided Services’ 16
 criterion, which is a truly important criterion, according to figure 7, for customer satisfaction, is the one that has the lowest priority for improvement as it has a high performance score but low correlation with the reuse intention. High secretarial support Modeled Importance educational process additional services provided services image Low Low Quality Rating High Figure 10: Customer motivation window of the educational organization 7. CONCLUDING REMARKS Real world examples have shown that free stated importance by the customer is often different than derived importance by a preference disaggregation model. In this paper, an extension of the MUSA methodology is presented. It allows modelling customer importance preferences for service characteristics and offers the ability to compare derived and modelled weights of the satisfaction dimensions. The results of MUSA extension application to an educational organization, give a representative example of the differentiation between derived and stated importance. References Allen D.R. and Rao T.R. (2000). Analysis of customer satisfaction data, ASQ Quality Press, Milwaukee. Berger C., Blauth R., Boger D et al. (1993). Kano’s methods for understanding customer defined quality, The Journal of the Japanese Society for Quality Control, Fall, pp. 335. Dick A. and Basu K. (1994). Customer loyalty: Toward an integrated conceptual framework, Journal of the Academy of Marketing Science, 22, (2), 99113. Griffin A. and Hauser J.R. (1993). The voice of the customer, Marketing Science, Winter, pp. 1 27. Grigoroudis E. and Siskos Y. (2002), Preference disaggregation for measuring and analysing customer satisfaction: The MUSA method, European Journal of Operational Research, (to appear). Hill N. (1996). Handbook of customer satisfaction measurement, Gower Publishing, Hampshire. 17
 Hinterhuber H.H., Aicher H. and Lobenwein W. (1994). Unternehmenswwert und Lean Management, ManzVerlag, Vienna. Jacoby J. (1978). Brand loyalty: Measurement and management, Wiley, New York. Jacoby Jacob, (1978), ‘Brand Loyalty: Measurement and Management’, Wiley, New York. JacquetLagrèze E. and J. Siskos (1982). Assessing a set of additive utility functions for multicriteria decisionmaking: The UTA method, European Journal of Operational Research, (10), 2, 151164. Kano N. (1984). Attractive quality and mustbe quality, The Journal of the Japanese Society for Quality Control, April, pp. 3948. Lowenstein M.W. (1995). Customer Retention – An integrated Process for Keeping Your Best Customers, ASQC Press, Milwaukee. Matzler K. and Hinterhuber H.H. (1998). How to make product development projects more successful by integrating Kano’s model of customer satisfaction into quality function deployment, Technovation, vol. 18, no. 1, pp. 2538. Matzler K., Hinterhuber H.H., Bailom F. and Sauerwein E. (1996). How to delight your customers, Journal of Product and Brand Management, vol. 5, no. 2, pp. 618. Naumann E. and Giel K., (1995), ‘Customer Satisfaction Measurement and Management: Using the voice of the customer’, Thomson Executive Press, Cincinnati. Oliva T.A., Oliver P.L. and Bearden W.O. (1995). The relationships among consumers satisfaction, involvement, and product performance: A catastrophe theory approach, Behavioral Science, 40, 104132. Oliva T.A., Oliver P.L. and McMillan I.C. (1992). A catastrophe model for developing service satisfaction strategies, Journal of Marketing, 56, 8395. Oliver R. L. (1996). Satisfaction: A behavioral perspective on the customer, McGrawHill, New York. Siskos Y. (1984). Le traitement des solutions quasioptimales en programmation linéaire continue: Une synthèse, RAIRO Recherche Opérationnelle, 18, 382401. Siskos Y. (1985). Analyses de régression et programmation linéaire, Révue de Statistique Appliquée XXXII, 4155. Siskos Y. and D. Yannacopoulos (1985). UTASTAR: An ordinal regression method for building additive value functions, Investigaçao Operacional, (5), 1, 3953. Siskos Y., (2000), ‘Linear Programming’, New Technologies, Athens. Tsiotras G., (1995), ‘Quality Improvement’, Benou Publishing, Athens. Vavra T.G. (1997). Improving your measurement of customer satisfaction: A guide to creating, conducting, analyzing, and reporting customer satisfaction measurement programs, ASQC Quality Press, Milwaukee. 18
CÓ THỂ BẠN MUỐN DOWNLOAD

Measuring Customer Satisfaction In The fastfood Industry
0 p  256  68

Customer Satisfaction Made easy
35 p  145  62

Measuring Customer Satisfaction
25 p  201  61

Customer satisfaction measurement and ISO 9001:2000
5 p  141  52

Customer Satisfaction Research
11 p  262  50

Customer Satisfaction Made Easy A plan for actively listening to customers of Labor Market Information Products and Services
35 p  138  38

Measuring Customer Satisfaction in Today’s Highly Competitive Environment
0 p  153  36

Final Report of the Customer Satisfaction Work Group of the Workforce Information Council Workforce Investment Act of 1998.
16 p  218  33

An Overview of Customer Satisfaction and Loyalty
5 p  176  23

Book Measuring Customer Satisfaction
0 p  133  20

The latest thinking on Customer Satisfaction Measurement
28 p  138  19

Conducting Statewide Customer Satisfaction Surveys
20 p  91  17

CUSTOMER SATISFACTION MEASUREMENT ISSUES IN THE FEDERAL GOVERNMENT
6 p  86  16

“Measuring customer satisfaction in the context of a projectbased organization”
0 p  145  15

DAS Customer Satisfaction Survey Report
21 p  145  15

1998 Customer Satisfaction Report
20 p  71  7

Customer Satisfaction Assessment at the Pacific Northwest National Laboratory
23 p  116  7