- 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.. - Measurement will provide the analysts with the highest and lowest performance indicators, pointing out the leverage opportunities and the weaknesses of the company.. - 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.. - 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.. - DIFFERENT METHODOLOGICAL APPROACHES FOR CUSTOMER SATISFACTION. - Kano’s model for customer satisfaction. - In many cases customer satisfaction has been seen mostly as a one-dimensional construction – the higher the perceived product quality, the higher the customer’s satisfaction and vice versa.. - 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.. - 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. - 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. - 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):. - Kano’s model of customer satisfaction can be optimally combined with quality function deployment. - 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. - where the value functions Y and are normalised in the interval [0, 100], and is the weight of the i-th criterion.. - Table 1: Variables of the MUSA method Y : client’s global satisfaction. - x i k : the k-th satisfaction level of the i-th criterion (k=1, 2. - y *m : value of the y m satisfaction level X * i : value function of X i. - x i *k : value of the x i k satisfaction level. - (2) Furthermore, because of the ordinal nature of Y and the following preference conditions. - The MUSA method infers an additive collective value function , and a set of partial satisfaction functions from customers’ judgements. - The main objective of the method is to achieve the maximum consistency between the value function Υ and the customers’. - is the estimation of the global value function Y , and and are the overestimation and the underestimation error, respectively.. - 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 (Siskos and Yannacopoulos, 1985. - (5) It is very important to mention that using these variables, the linearity of the method is achieved. - 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. - The average of the optimal solutions given by the n LPs (7) may be considered as the final solution of the problem. - 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: C 1 = very important criterion, C 2 = important criterion, C 3 = less important criterion.. - Considering that C 1 , C 2 , C 3 are ordered in a 0 to 100% scale, there are two preference thresholds T 1 and T 2 , which define the % rate, which distinguishes each one of the three categories (see. - 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. - In this way, the importance of each criterion according to customers’ preferences can be assessed and compared with the results of the MUSA method.. - where S ij + and S ij - are the overestimation and underestimation error, respectively, for the i-th criterion of the j-th customer, C 1 , C 2 , C 3 are the customers’ preference categories, T 1 and T 2 are the preference thresholds, α i is the number of satisfaction scale levels for i criterion, and w it is a MUSA variable.. - Those linear programs maximize the weights b i of the criteria and have the following form:. - 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. - ASI is the mean value of the normalized standard deviation of the estimated weights b i and is calculated as follows:. - 1 1 , where b is the estimated weight of the criterion i, in the j-th post-optimality analysis LP (Grigoroudis and Siskos, 2002).. - 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 pay-off matrix. - Stage A: Solution of the following linear program min] F 1. - where F 1 * is the optimal solution of the objective function of LP (11) and ε is a small percentage of F 1. - Stage C: At this post-optimality analysis stage n linear programs are formed and solved, one for each of the n satisfaction criteria. - where ε 1 and ε 2 are small and positive numbers, F 1 * and F 2 * are the optimal solutions of the objective functions of LP (11) and LP (12), respectively.. - 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). - 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.. - 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.. - 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. - 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. - 6.1 Results of the weight estimation model. - Different values of λ for the two constraints T2≥λ and T1-T2≥λ of the linear program 8. - 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.. - 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 criteria of ‘Educational Process’ and ‘Secretarial Support’ should be further examined since they appear in between quadrants IV-I and III-II 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. - Figure 7: Dual importance window of the educational organization. - 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.. - The results of the heuristic method described in paragraph 4.2 with F 1 * ≤10 and F * ≤3036 are presented in table 4. - of ‘Provided Services’, ‘Educational Process’, and ‘Secretarial Support’ as the most 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. - As a conclusion, MUSA extension can sometimes improve the MUSA model.. - A further analysis for the examination of the relationship that the different type of attributes (one-dimensional, 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. - Table 5: Correlation of criteria with the intention to reuse the services of the educational organization. - 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. - 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. - The ‘Additional Services’ and the ‘Image’ criteria, which are the truly. - Figure 10: Customer motivation window of the educational organization 7. - 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.. - Analysis of customer satisfaction data, ASQ Quality Press, Milwaukee.. - Kano’s methods for understanding customer- defined quality, The Journal of the Japanese Society for Quality Control, Fall, pp. - Customer loyalty: Toward an integrated conceptual framework, Journal of the Academy of Marketing Science . - The voice of the customer, Marketing Science, Winter, pp. - Handbook of customer satisfaction measurement, Gower Publishing, Hampshire.. - Attractive quality and must-be quality, The Journal of the Japanese Society for Quality Control, April, pp. - and Giel K Customer Satisfaction Measurement and Management: Using the voice of the customer’, Thomson Executive Press, Cincinnati.
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