Customer Satisfaction Study Via a Latent Segment Model (earlier version of the article published in JRCS) (Cite as: Fonseca, Jaime R. S. (2009), Customer Satisfaction Study via a Latent Segment Model, Journal of Retailing and Consumer Services, 16, 352-359) Jaime R. S. Fonseca ISCSP-Technical University of Lisbon, Portugal jaimefonseca@iscsp.utl.pt Abstract: This study evaluates customers’ satisfaction of a certain public organization. In order to estimate the global customers’ satisfaction measure, we must appeal to methodologies recognizing that satisfaction must be understood as a latent variable, quantified through multiple indicators. Thus, it is natural that we consider the latent segment models approach to proceed to the evaluation of customer’s service satisfaction. As a result of these models estimation, we selected a three latent segment model: segment 1, with 50% of the customers, that represents "The Very Satisfied ", for those to whom everything is very well in the organization; a segment 2, with 33% of the customers, representative of the "The Well Satisfied", not totally satisfied with the quality of the organization, and a segment 3, with 16% of the customers, “Satisfaction Demanders”, thinking that organizational quality can be improved. We developed an Overall Satisfaction Index (OSI) which is important to show how the company as a whole is performing. Keywords: Service quality, American Customer Satisfaction Index, Customer satisfaction, Overall Satisfaction Index, Latent segment models, Information criteria. 1 Introduction Customer satisfaction is central to the marketing concept, with evidence of strategic links between satisfaction and overall service performance (Truch, 2006), and is an important theoretical and practical issue for most marketers and consumer researchers (Goode, 2001); it is a key issue for all those organizations that wish to create and keep a competitive advantage in this highly competitive world. Customer satisfaction which remains in the limelight (Bartikowski & Llosa, 2004), especially in the service field, is typically defined as an overall assessment of the performance of various attributes that constitute a service. The organization wants to know how satisfied their customers are, in order to be translated into marketing strategy and organizational development. First, it was important to understand the ways that services can influence customer behaviour in terms of satisfaction, so that we may achieve a consistent customer satisfaction measure, knowing that satisfaction level increases as the congruence between the organization’s goals and the customers’ interest also increases, (Garbarino & Johnson, 2001). This service is a non profit professional service, whose customers are the organization’s employees and organization’s retired employees. For the organization management, customer satisfaction could be indirectly measured by means of several response determinants (e.g. performance, equity, expectation, disconfirmation, attribution, etc.), and these impacts on satisfaction are heterogeneous, (Wu & DeSarbo, 2005). Literature review and conceptualization of service quality Service quality has been studied for a long time. However, the service quality literature also suggests that there is no consensus on how to conceptualize perceived service quality (Caro & García, 2007), and two different approaches have been adopted regarding this issue, mainly because of the difficulties involved in delimiting and measuring the construct (Parasuraman, Zeithaml, & Berry, 1985). The first one suggests that perceived service quality is based on the disconfirmation paradigm (by a comparison between customers’ expectations and their perceptions of the received service) (Gronroos, 1984) and (Parasuraman et al., 1985). The second approach suggests that service quality should be measured considering only customer perceptions rather than expectations minus perceptions (Caro & García, 2007). Nowadays we can see a movement away from using expectations, and the theoretical background of service quality is moving from expectation disconfirmation to the theory 2 of reasoned action which states that individuals’ behaviour can be predicted from their intentions, which can be predicted from their attitudes about the behaviour and subjective norms (Collier & Bienstock, 2006). It is well known that service quality and customer satisfaction are distinct constructs, (Dabholkar, 2000). Another important question was answered by (Oliver, 1993), which first suggests that service quality would be antecedent to customer satisfaction regardless of whether these constructs were measured for a given experience or over time. (Spreng & Macoy, 1996) find empirical support for this model, wherein customer satisfaction is a consequence of service quality, and (Dabholkar, 2000) proves that customer satisfaction is a consequence of service quality (Mediator Model of Customer Satisfaction). The results of (Bodet, 2006) suggest that the quality of human factors, such as staff behaviour, and non-tangible factors, such as image, are determinant in the formation of customer satisfaction. In this sense, by knowing customers’ perceptions about service quality we think that we can measure customer’s service satisfaction, using service quality as an indirect approach to customer satisfaction. Because of the difficulty on measuring the customers’ expectations about a service quality (can they have expectations about unknown services?), we think that quality is about conformance to a service design or service specification. Once the design is set, quality is about ensuring that the end deliverable to the customer meets this specification or design. As a consequence, from a service point of view, customer satisfaction is about monitoring the quality of delivery of the service, thus measuring how well the organization is delivering the providing service. Services can only be experienced, and the production of a service takes place at the same time and in the same place as its consumption. The perception of service quality by customers during service delivery will be influenced mainly by three factors: technical quality (what the supplier delivers), result of know-how available to the organization, with objective evaluations; functional quality (how the supplier delivers), representing the way the service is provided (staff appear to be a key element in the service encounter and more precisely their capacity to answer or solve problems encountered by the customer on the premises, (Bodet, 2006)); the image of the organization which is delivering the service, and the supplier’s corporate image. In order to provide insights for marketing managers to make better customer satisfaction measurement decisions, we think that service performance, with technical quality, 3 functional quality and corporate image, is the best determinant of overall customer satisfaction in this particular service (Fig.1). Figure 1 Model conceptualization Latent TECHNICAL QUALITY → FUNCTIONAL QUALITY → CORPORATE IMAGE → CUSTOMER SATISFACTION Indicators Concerning this service, we think that increasing service performance is the key to increasing customer satisfaction (all the coefficient correlations between technical quality and functional quality are significant at the 0.01 level, table 10). An important theoretical advantage of this approach is that its results are derived from actually experienced services performances. Measures and methodology The organization uses a survey tool to collect these data from their key customer base, that is to say the target population being the visitors/customers to the service. All of the customers are post experience, because they can only be satisfied or not with the service, having experienced it. Because the number and nature of service quality dimensions is in direct relation to the service under analysis, the questionnaire used in this study was designed through a lot of discussions with the organization manager, after careful literature review. We use a careful questionnaire about different aspects of the service because the more detailed the information is, the more useful it is likely to be for improving the service. As a preliminary scale we use a set of 23 items representing all relevant facets of service quality as input to customer satisfaction. By using focus group interviews with Technical University of Lisbon students, we simplified the scale, by eliminating some confusing items, and reworded others. The final questionnaire had eighteen items with a 10-point Likert-type scale, ranging from 1 4 (indicates an extremely negative classification, not at all satisfied with service quality) to 10 (indicates an extremely positive classification, completely satisfied with service quality). Because overall satisfaction depends on how the customers experience the quality of different aspects like, for example, service quality and expectations, reception and welcome, professional reliability, the orientation to the customer, we used the attributes we present in (table 1). These variables are the indicator variables or segmentation base variables, and the LSM is indicated because we have no response variable on global satisfaction in the questionnaire to indicate as dependent variable. For assessing content validity, the survey questionnaire was subjected to pre-test and refinement trough a pilot study of 70 randomly selected customers. Data was obtained from the individuals, with a self-administered questionnaire. An initial sample of 873 respondents was obtained, but 17 questionnaires were considered non-valid. The final sample was representative of the individuals’ population heterogeneity with regard to demographic characteristics such as service, customer kind, gender and education. Table 1 The survey items Technical Quality Functional Quality Corporate Image Socio-demographic Service Quality and Demand Service Quality and Expectation The Ideal Public Service Global Quality of Services Reception and Welcome Services area Services Location Services Identification Professional knowledge Information clarity Waiting Time Liking, attention and professional interest Professional reliability Interest on customer Modernity Credibility The orientation to the customer The spreading of the services Service Customer kind Gender Education 5 We run factor analysis with these items, in order to see if they were structurally related. The value of KMO (Kaiser-Meyer-Olkin) measure, 0.879, and Bartlett’s test of Sphericity (any p-value) indicate that the data is suitable for factor analysis application. As we can see, almost all of the factor loadings of the items are significant (table 2) and so, Technical quality, Functional quality and Corporate image could be viewed as the core value items of the service in increasing overall customer satisfaction for the further analysis. The results for Principal Component Analysis (tables 2 and 3) showed that the items of table 1 loaded on three factors (eigenvalues over 1) as displayed in table 3, rotated component matrix. Those factors that explained 74 percent of the meaningful variation in the initial items, roughly represent Technical quality, Functional quality and Corporate image. Table 2 Items and Factor Loadings Items Factor Loadings Customer kind ,529 Gender ,587 Education (School attendance) ,674 Service Location ,545 Service Identification ,718 Reception and Welcome area ,869 Services área ,833 Professional knowledge ,857 Information clarity ,867 Waiting Time ,729 Liking, attention and interest ,852 Professional reliability ,817 Interest in customer problems ,830 Modernity of procedures and technological supports ,495 Credibility and inspired confidence ,691 Orientation to serve the customer Spreading of given services ,744 Service Quality and Demand ,786 Service Quality and Expectation ,781 The Ideal Public Service ,682 Global Quality of Services ,730 ,531 6 Table 3 Rotated Component Matrix(a) Component 1 2 3 Service Location 0,03 0,58 0,37 Service Identification 0,11 0,84 0,18 Reception and Welcome area 0,26 0,87 0,06 Services área 0,36 0,81 0,15 Professional knowledge 0,86 0,23 0,26 Information clarity 0,88 0,20 0,25 Waiting Time 0,82 0,15 0,17 Liking, attention and interest 0,90 0,10 0,19 Professional reliability 0,88 0,15 0,20 Interest in customer problems 0,87 0,19 0,22 Modernity of procedures and technological supports 0,19 0,64 0,18 Credibility and inspired confidence 0,64 0,40 0,32 Orientation to serve the customer 0,45 0,28 0,71 Spreading of given services 0,11 0,53 0,44 Service Quality and Demand 0,38 0,28 0,80 Service Quality and Expectation 0,34 0,27 0,82 The Ideal Public Service 0,67 0,27 0,40 Global Quality of Services 0,60 0,22 0,58 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. (a) Rotation converged in 5 iterations. The model and model selection Many organizations have felt the critical need to use a tool for evaluating service quality in order to appropriately assess and improve their service performance and consequently improve customer satisfaction. Even thought customer satisfaction cannot be directly observed, it is possible to employ indicators to capture empirically the construct, (Anderson & Fornell, 2000). This study measures customer satisfaction in a public administration service, based on a questionnaire inspired on the ACSI (American Customer Satisfaction Index) model. This index, measuring overall customer satisfaction, is a customer evaluation tool for aspects that cannot be measured directly, (Anderson & Fornell, 2000). So, the methodology used to analyse data must recognize this and must be able to measure this latent variable (customer satisfaction) by multiple observable indicators. This article 7 examines a customer satisfaction model for assessing the relationship of overall satisfaction with a service. Thus, we propose measuring customer’s satisfaction indirectly as a latent variable, by the used indicators, estimating Latent Segment Models (LSM), assuming that there is heterogeneity, which is natural in services because a service supplier consists of different staff all working on the provision of the same services. That is, we suggest market segmentation for customer satisfaction study, via LSM, because it provides a probabilistic methodology for segmentation, based on the indicator (observable) variables. Homogeneity within the segment is critical to defining the target customer. In LSM, customers that exhibit the most similar attributes’ results would be grouped in one segment. Heterogeneity across the segments allows for the differentiation of segments and customers. The Latent Segment Model was initially introduced by (Lazarsfield & Henry, 1968) as latent class model, assuming that the latent variable is categorical. LSM (e.g., (Cohen & Ramaswamy, 1998), (Fonseca & Cardoso, 2005)), are used to identify the latent segments required to explain the associations among a set of observed variables (segmentation base variables) and to allocate observations to these segments. It represents a model-based approach to clustering, which connects clustering with classical statistical estimation methods, and assumes that the variables’ observations in a sample arise from different segments of unknown proportions. Customer heterogeneity in satisfaction requires marketing managers to segment the market and make segment-specific customer satisfaction measurement decisions. The use of LSM has become increasingly popular in the marketing literature as for instance (Wedel & Kamakura, 1998), (Dillon & Kumar, 1994), (Bhatnagar & Ghose, 2004). This approach to segmentation offers some advantages when compared with other segmentation techniques: it identifies market segments, and provides unbiased market segment memberships estimates, (Dillon & Kumar, 1994); it provides means to selecting the number of segments, (McLachlan & Peel, 2000); it is able to deal with different measurement levels, (Vermunt & Magidson, 2002); demographic and other covariates can be used for segment description, (Magidson & Vermunt, 2003); it allocates cases into segments based upon membership probabilities estimated directly from the model, instead of using an ad-hoc definition of “distance” (e.g., Euclidian distance), (Bonilla & Huntington, 2005). 8 The primary focus and contribution of this manuscript is to present a latent segmentation methodology in order to provide a way for marketing managers to make segment-specific decisions in customer satisfaction measurement. This methodology accommodates multiple performance attributes (including mixed case), provides parsimonious model in order to account for the relationships between these multiple performance’s attributes, and derives latent service segments based on these attributes or indicators of customer satisfaction. Let y  ( yip ) denote the vector representing the scores of the ith case for the pth i segmentation base attributes (i = 1,…,n ; p = 1,…,P). We consider that the cases on which the attributes are measured arise from a population which we assume to be a mixture of S segments, in proportions  s (mixing proportions or relative segment sizes), s = 1,…,S. The statistical probability density function of the vector y i , given that y i comes from segment s, is represented by f s ( y i |  s ) , with  s representing the vector of unknown parameters associated with the specific pdf chosen. Then the population density can be represented as a finite mixture of the densities f s ( y i |  s ) of S distinct segments, i.e. f ( y | )  i  s S s 1  f s ( yi |  s ) P p 1 (1) where i = 1,…,n,   0,   s  1,   { , }, with   {1 ,, s 1} ,   { 1 ,, s } , S s and s 1  is the vector of all unknown parameters. The LSM estimation problem, simultaneously addresses the estimation of distributional parameters and classification of cases into segments, yielding mixing probabilities. The estimation process is typically directed to maximum likelihood using the Expectation-maximization (EM) algorithm, (Dempster, Laird, & Rubin, 1977), (McLachlan & Peel, 2000), (Figueiredo & Jain, 2002). LSM naturally provides means for constituting a partition by means of assigning each case to the segment with the highest segment-membership probability, Max ˆis , where s 1,...,S ˆ is  ˆs ( y |  i (k ) )  ˆ (k ) s f s ( y i | ˆ (k ) s ) S (k ) (k )  ˆ f j ( y i | ˆ ) j j 1 j (2) 9 In order to derive meaningful results from clustering, the mixture model must be identifiable, that is, a unique maximum likelihood solution should exist, (Bozdogan, 1994). A goal of traditional LSM estimation is to determine the smallest number of latent segments S that is sufficient to explain the relationships observed among the variables of segmentation base variables. If the baseline model (S = 1) provides a good fit to the data, no LSM is needed, since there is no relationship among the variables to be explained; otherwise, a model with S = 2 segments is then fitted to the data. This process continues by fitting successive LSM to the data, every time adding another dimension by incrementing the number of segments by 1, until a parsimonious model is found that provides an adequate fit. In order to select the best number of segments and in an attempt to overcome most of the limitations of likelihood ratio tests (regularity conditions in finite mixtures do not hold), (Ramaswamy, Chaterjee, & Cohen, 1996), theoretical information criteria can be used. They assist in determining the adequate value of S based on minimum criteria values. The general form of information criteria is - log L(ˆ )  C . The first term is the negative logarithm of the maximum likelihood which decreases when model complexity increases; the second term, or penalty term, penalizes too complex models, and increases with the model number of parameters ( n ).The selected LSM should evidence a good trade-off between good description of the data and the model number of parameters. The second focus and contribution of this manuscript consists on using an adequate information criterion for selecting the best LSM. In a recent work, (Fonseca & Cardoso, 2007), based on an empirical analysis, evidences the good performance of AIC 3 when dealing with only categorical segmentation base variables which is defined by  2 log L (ˆ )  3 n  , (Bozdogan, 1987). Results and Discussion As a result of LSM estimation, we selected, by means of the used information criterion AIC3, a three-segment solution, as displayed in table 4, because AIC3 minimizes for S = 3. 10 Table 4 Information criterion values Model AIC3 1-latent segment 2- latent segment 3- latent segment 4- latent segment 8381,9 7278,8 7093,4 7152,2 Tables 5 to 7 display two kinds of probabilities, estimated by LSM; the probabilities s (s = 1,…,S) of belonging to segment s, and probabilities f s ( y i |  s ) , of being on a variable category, conditional on belonging to a segment s. For instance, the probability 0.70 (bold, in table 5) represents the probability of an individual that answer 10 for Service Quality and Demand, given that it belongs to segment 1. The estimates of conditional probabilities displayed in tables 5 to 7 allow us to name the three segments as follows. A segment 1, with 50.4% of customers, which represents The Very Satisfied (Top); for them, everything is very well in the organization. By the customer’s technical quality profile (table 5), we can see that they classify all the items with ten. For them, the service quality is extremely positive in all items of technical quality, and they are naturally very satisfied. Concerning functional quality (table 6), again, they consider the staff as extremely positive. As for corporate image (table 7), they also consider the service image as extremely positive. Thus, customers of segment 1 belong to a high level of customer satisfaction. In opposite, we have segment 3, with 16.2% of customers, The Satisfaction Demanders, thinking that service quality can be improved. In terms of technical quality (table 5), they classify service quality and demand with 6 to 8, service quality and expectation with 5 to 8, the ideal public service with 4 to 7, and global quality of services with 6 to 8. 11 Table 5 Customers’ Technical Quality profile Relative Segment Size (s) VARIABLES Service Quality and Demand 6 7 8 9 10 Service Quality and Expectation 5 6 7 8 9 10 The Ideal Public Service 4 5 6 7 8 9 10 Global Quality of Services 6 7 8 9 10 Reception and Welcome 3 5 6 7 8 9 10 Service area 4 5 6 7 8 9 10 Service Location 1 3 4 5 6 7 8 9 10 Service Identification 3 4 5 6 7 8 9 10 Segment 1 50.4% Segment 2 33.4% Segment 3 16.2% 0,01 0.00 0,04 0,25 0,70 0.00 0,04 0,20 0,67 0,08 0,08 0,37 0,45 0,09 0,01 0,01 0.00 0,01 0,04 0,29 0,65 0,02 0,02 0,08 0,32 0,48 0,08 0,04 0,17 0,29 0,37 0,13 0.00 0.00 0.00 0.00 0.00 0,05 0,29 0,66 0.00 0,02 0.00 0,08 0,26 0,60 0,04 0,04 0,12 0,21 0,29 0,25 0,09 0.00 0,01 0.00 0,01 0,24 0,74 0,02 0.00 0,16 0,60 0,22 0,12 0,41 0,37 0,09 0,01 0.00 0,01 0,01 0,04 0,25 0,24 0,45 0.00 0,02 0,10 0,18 0,38 0,28 0,04 0,04 0,17 0,10 0,25 0,33 0,09 0,04 0.00 0.00 0,01 0,02 0,25 0,24 0,47 0.00 0,02 0,10 0,12 0,30 0,44 0,02 0,04 0,16 0,16 0,21 0,37 0.00 0,04 0,01 0,01 0.00 0,04 0,05 0,12 0,26 0,26 0,24 0,03 0,04 0,04 0,11 0,10 0,16 0,26 0,22 0,04 0.00 0.00 0.00 0,08 0,16 0,21 0,54 0.00 0.00 0,01 0,01 0,01 0,08 0,08 0,26 0,21 0,33 0,02 0,02 0,10 0,14 0,16 0,22 0,28 0,06 0,04 0,04 0,17 0,17 0,21 0,29 0,08 0.00 12 About reception and welcome, they classified from 3 to 7, and services area from 4 to 8; as for services location, these customers classified it from 6 to 8, but they classify services identification from 3 to 8. Table 6 Customers’ Functional Quality profile Relative Segment Size (s) VARIABLES Professional knowledge 6 7 8 9 10 Information clarity 6 7 8 9 10 Waiting Time 5 6 7 8 9 10 Liking, attention and interest 6 7 8 9 10 Professional reliability 6 7 8 9 10 Interest in customer problems 6 7 8 9 10 Segment 1 50.4% Segment 2 33.4% Segment 3 16.2% 0,00 0,00 0,00 0,01 0,98 0,00 0,00 0,20 0,72 0,09 0,12 0,33 0,37 0,09 0,09 0,00 0,00 0,00 0,07 0,93 0,00 0,00 0,14 0,77 0,09 0,16 0,25 0,49 0,05 0,05 0,00 0,00 0,00 0,05 0,11 0,84 0,02 0,00 0,00 0,12 0,65 0,21 0,08 0,08 0,29 0,33 0,05 0,17 0,00 0,00 0,00 0,03 0,97 0,00 0,00 0,04 0,65 0,31 0,08 0,12 0,41 0,21 0,17 0,00 0,00 0,00 0,00 1,00 0,00 0,02 0,04 0,71 0,23 0,04 0,25 0,45 0,09 0,17 0,00 0,00 0,00 0,07 0,93 0,00 0,00 0,20 0,65 0,15 0,04 0,37 0,41 0,13 0,05 Thus a few of them considered negative the organization performance on some items (the ideal public service, reception and welcome, services area and services identification). 13 Table 7 Customers’ Corporate Image profile Relative Segment Size (s) VARIABLES Segment 1 Segment 2 Segment 3 50.4% 33.4% 16.2% Modernity of procedures and technological supports 5 6 7 8 9 10 Credibility and inspired confidence 5 6 7 8 9 10 Orientation to serve the customer 5 6 7 8 9 10 Spreading of given services 5 6 7 8 9 10 su 0,03 0,07 0,03 0,25 0,29 0,34 0,04 0,14 0,16 0,34 0,28 0,04 0,04 0,21 0,50 0,21 0,00 0,04 0,00 0,00 0,00 0,07 0,30 0,63 0,00 0,04 0,04 0,36 0,56 0,00 0,00 0,16 0,37 0,42 0,00 0,05 0,00 0,00 0,01 0,05 0,22 0,71 0,00 0,00 0,06 0,34 0,58 0,02 0,04 0,12 0,37 0,37 0,05 0,05 0,05 0,08 0,09 0,15 0,15 0,49 0,02 0,08 0,18 0,32 0,32 0,08 0,12 0,29 0,29 0,29 0,00 0,00 Concerning functional quality (table 6) they classified professional knowledge from 6 to 8, information clarity from 6 to 8, waiting time from 5 to 8, liking, attention and professional interest from 6 to 8, professional reliability from 6 to 8, and interest in customer problems from 6 to 8. So, for everything they classified as positive. As far as corporate image is concerned (table 7) they classified modernity of the procedures and technological support from 5 to 7, credibility and inspired confidence from 5 to 8, the orientation to serve the customer from 5 to 7, and the spreading of the given services from 5 to 7. Thus, even for these customers almost everything is slightly positive. Between these two segments we have segment 2, with 33.4% of customers, The Well Satisfied. They are always less demanding customers than those of segment 1, and almost always more demanding customers than those of segment 3, except for services location. We display customers’ socio-demographic profile, (table 8), in order to a better customers’ service satisfaction understanding. 14 Table 8 Customers’ socio-demographic profile Relative Segment Size (s) VARIABLES Service Military Staff Social Share Customer kind Reserve Retired Widowers or Relatives Pensioners Availability Gender Male Female Education (School attendance) Primary school Basic school Secondary school Higher Education Segment 1 50.4% Segment 2 33.4% Segment 3 16.2% 0,33 0,67 0,62 0,38 0,59 0,42 0,12 0,49 0,18 0,11 0,11 0,18 0,42 0,16 0,04 0,20 0,12 0,62 0,04 0,08 0,13 0,79 0,20 0,76 0,22 0,87 0,13 0,04 0,54 0,23 0,18 0,06 0,44 0,28 0,22 0,04 0,33 0,25 0,37 Thus we have in segment 1 social share service customers, widowers or relatives and pensioners, and basic school customers; in segment 2, customers are majority military customers, on reserve or on the availability, majority female, and customers with secondary school; in segment 3, we have retired customers, majority male customers, with higher education. Finally, we develop an overall satisfaction Index, in order to measure overall customer satisfaction, accordingly to their responses. Let the Mean Satisfaction Rating (table 9) be the mean of all responses to each item that sums 162 for all items. Then, the Weighted Factor (WF) is the result of MSR dividing by 162. Next we have Weighted Scores (WS) that results from the multiplication of each MSR by each WF. All the WS sum to the Overall Weighted Average. If Overall Satisfaction Index (OSI) achieved satisfaction scores of ten out of ten on every considered items, the Overall Weighted Average would be ten, and we express OSI as a percentage of that theoretical maximum score. This index is a very important one, from managerial perspective, because it shows how the Company as a whole is performing. 15 Conclusion and managerial implications Managers interested in building customers’ satisfaction may seek for a better understanding of customers’ behavioural satisfaction, in order to focus on possible marketing actions to improve or maintain customer satisfaction. The finding results help on developing a picture of the customers of this Public Service, about satisfaction. Moreover, the LSM, based on observed categorical variables, enables us to analyse organizational quality and customer satisfaction, which is an important theoretical and practical issue for organization managers. Thus, in a managerial sense, those results are actually quite opportune, because organization manager became knowledgeable of how customers’ satisfaction goes on, and so results obtained from this study can result in strategic planning strategies enhancing customers’ satisfaction. It was learned that service customers perceive high satisfaction. Most customers surveyed (50.4 percent, the majority) appear to be very satisfied with service quality. The analysis allows us to conclude that about 83.8 percent (segment 1 and segment 2) of customers surveyed revealed highly overall satisfaction. Table 9 Overall Satisfaction Index Items Service Location Service Identification Reception and Welcome area Service area Professional knowledge Information clarity Waiting Time Liking, attention and interest Professional reliability Interest in customer problems Modernity of procedures and technological supports Credibility and inspired confidence Orientation to serve the customer Spreading of given services Service Quality and Demand Service Quality and Expectation The Ideal Public Service Global Quality of Services Overall Weighted Average Overall Satisfaction Index Mean Satisfaction Rating 7,73 7,94 8,42 8,50 9,28 9,22 9,20 9,48 9,42 9,27 8,12 8,88 8,92 8,18 9,01 8,85 8,82 9,13 162 Weighting Factor 4,77 4,90 5,20 5,25 5,73 5,69 5,68 5,85 5,82 5,72 5,01 5,48 5,50 5,05 5,56 5,46 5,44 5,64 Weighted Score 0,37 0,39 0,44 0,45 0,53 0,52 0,52 0,55 0,55 0,53 0,41 0,49 0,49 0,41 0,50 0,48 0,48 0,51 8,71 87% 16 The findings reported here suggest that overall customer satisfaction is real and so the service quality is very good for the customers’ majority. It is supported by the latent segments, as discussed, and moreover, by the OSI value of 87 percent (table 9). Professional knowledge Information clarity Waiting time Liking, attention and interest Professional reliability Interest on customer problems Table 10 Technical versus Functional Spearman correlations Service Quality and Demand 0.68** 0.70** 0.57** 0.62** 0.64** 0.69** Service Quality and Expectation 0.68** 0.67** 0.58** 0.55** 0.58** 0.64** The Ideal Public Service 0.65** 0.67** 0.51** 0.52** 0.61** 0.63** Global Quality of Services 0.68** 0.68** 0.51** 0.63** 0.61** 0.68** ** Correlation is significant at the 0.01 level (2-tailed). This study also focus on identifying socio demographic factors underlying the different reinforcing behaviours across customer segments, providing managers with even more information. From customers’ socio-demographic profile we can see that gender is not relevant for overall satisfaction and the same happens for customers with middle education. Managers must care with segment 2 (33.4 percent) customers, and mostly with segment 3 (16.2 percent) customers, because they are not very satisfied, or they are unsatisfied, respectively. Because the organization core business is the staff, we correlate technical quality with functional quality (see table 10); the significant Spearman correlations (p-value < 0.01) highlight the human resources (staff) as the service face, as it should be in such an organization. Thus, as (Agus, 2004), we also think that managers should emphasize the importance of teamwork in achieving the service goals, that is to say, every worker must be empowered to act on worthwhile suggestions that will ultimately improve customer satisfaction. 17 Limitations and future work We think that this Public Service can be representative of the Portuguese Public Sector. And so the conclusions may be valid for other Portuguese Public Services. 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