Customer Satisfaction - Term Paper Example

Customer Satisfaction Introduction Customer satisfaction is a major problem for every organization that wants to raise the value of customer possessions and make a better business piece. To enhance the value of customer assets, customer satisfaction must be precise and managed. The dominant conceptual model in the customer satisfaction area is the disconfirmation of expectations model. Here customer satisfaction is an evaluative reaction of the product acquisition and consumption knowledge resulting from a contrast of what was predictable and what is conventional. But expectation is a very complex concept, and has often been the subject of various theoretical discussions as well as empirical verifications, revolving about: conceptual definitions of expectations; predictive contra normative expectations; expectations as the norm for comparison; expectations hierarchy; aspects that indirectly have an influence on expectations; absolute contra relative level of expectations; time for measuring expectations. Several empirical studies have highlighted the effect of expectations on customer satisfaction. The overall conclusion drawn from these studies is that expectations influence customer satisfaction, and the effect can be positive, negative or non-existent. But it can also be concluded that the positive as well as the negative effect of expectations on customer satisfaction is minimal. We believe partly that expectations is such a complex concept that it is hard to achieve reliable and valid measures, and partly that expectations as a concept does not have a conclusive influence on the formation of customer satisfaction. We suggest that expectations be dismissed from customer satisfaction measurement instruments in the future. We state that perceived quality is one of the primary drivers of customer satisfaction. Several empirical studies support these viewpoints. We agree with Gronross (1993,p. 61) that "it does not seem possible to make independent measurement of customer expectations ... It seems valid, at least in certain situations, to develop measurement models based on customer experiences of quality only". Cronin and Taylor (1992) and Liljander and Strandvik (1992) take the same view. The purpose of this paper is to examine empirically to what extent expectations have a measurable influence on the formation of customer satisfaction. Two Danish studies have been carried out. First, an experiment where the relationships between expectations, perceived quality and customer satisfaction were studied, using the methodology from the Swedish and American customer satisfaction index. Second, a customer satisfaction survey, using the methodology for the new European customer satisfaction index (ECSI). The purpose is also to highlight whether buying behavior, described by a set of relevant product category characteristics (price, complexity and sign value), has any influence on the relationship between perceived quality and customer satisfaction, and if so, how strong this influence is. Do some buying characteristics intensify such a relationship? The customer satisfaction process Previous research argues and supports different processes of customer satisfaction formation, and for our purpose we have systematized the findings in five models with different relationships between customer satisfaction and its drivers. Model 1 Model 1 is based upon one of the most popular theories and model structures used within the field of customer satisfaction formation, namely the disconfirmation of expectations theory. The disconfirmation concept will not enter the model as a variable--as it is the case in the disconfirmation of expectations theory--but will only be a constituent part of the measurement variables under customer satisfaction. Still, we believe that the theoretical arguments can be transformed to model 1, describing a modified disconfirmation of expectations theory. The disconfirmation concept should according to our terminology be interpreted as perceived disconfirmation. Perceived disconfirmation is the subjective evaluation of the difference between expectations and perceived quality carried out by the customer. The theory is described well in the literature and often empirically verified, for instance by Oliver (1977,1980,1981), Anderson (1973), Churchill and Suprenant (1982), Bearden and Teel (1983), Woodruff et al. (1991), Oliver and DeSarbo (1988) and Spreng and Olshavsky (1993). Model 2 Some research studies have not been able to find a direct effect of expectations on customer satisfaction--only an indirect effect through perceived quality and disconfirmation. Anderson and Sullivan (1993) found empirically that: (1) customer satisfaction is best modeled as a function of perceived quality and disconfirmation; (2) expectations do not have a direct effect on customer satisfaction--only indirectly via perceived quality and disconfirmation; (3) the more simple it is to evaluate quality, the more often disconfirmation will occur. Based upon these results, Anderson and Sullivan (1993) conclude that perceived quality has a larger impact on customer satisfaction than normally assumed in the traditional disconfirmation of expectation theory. Therefore, the authors develop a model where expectations have a direct and positive effect on perceived quality, but only an indirect effect on customer satisfaction via perceived quality and disconfirmation. Model 3 Churchill and Suprenant (1982) found in an empirical study for a fast moving consumer good that: (1) expectations have a negative effect on disconfirmation--the higher expectations, the lower perceived disconfirmation; (2) perceived quality has a positive effect on disconfirmation--the higher perceived quality, the higher perceived disconfirmation; (3) perceived disconfirmation has a positive effect on customer satisfaction--the more perceived quality exceeds expectations, the higher customer satisfaction; (4) both expectations and perceived quality have a direct effect on customer satisfaction. The three drivers of customer satisfaction explain 78% of the total variation in satisfaction. This empirical study supports model 3. Several studies found a direct effect of perceived quality on customer satisfaction, i.e. Churchill and Suprenant (1982), Oliver and DeSarbo (1988) and Tse and Wilton (1988). Furthermore, Churchill and Suprenant (1982) and Tse and Wilton (1988) found that the effect of perceived quality on customer satisfaction is higher than the effect of disconfirmation. Regarding the effect of expectations on customer satisfaction, some empirical studies found a direct effect of expectations on customer satisfaction, i.e. Bearden and Teel (1983), Churchill and Suprenant (1982), Oliver and Linda (1981), Swan and Trawick (1980), Tse and Wilton (1988) and Westbrook and Reilly (1983). Model 4 Studies have produced empirical evidence that perceived quality alone has a direct influence on the formation of customer satisfaction, i.e. Anderson and Sullivan (1993), Churchill and Suprenant (1982), Johnson and Fornell (1991) and Tse and Wilton (1988). Churchill and Suprenant (1982) studied a durable good and found that: (1) neither expectations nor disconfirmation have any effect on customer satisfaction; (2) only perceived quality influences how satisfied customers are. If customers have a positive quality experience, they are satisfied. If they have negative experience, they are dissatisfied--no matter what kind of initial expectations they had in advance. Perceived quality explains 88% of the total variation in customer satisfaction. If this study gives a basis for more general conclusions, disconfirmation will have only a tiny or no effect on customer satisfaction for durable goods. Customers' expectations remain passive and do not create disconfirmation. In some situations customers will not actively evaluate the quality. Oliver (1997) believes that customers who continually use a service will have expectations that remain passive, and therefore disconfirmation will never arise. Customers are simply not motivated to evaluate the quality every time the product is bought or used. We believe this is what happens for a product such as washing powder--here customers draw on earlier product experiences when creating their level of satisfaction. Johnson and Fornell (1991) studied the influence of product experience on the relationship between expectations, perceived quality and customer satisfaction and found: (1) the relationship between experience and customer satisfaction is positive--the more experience the customer has with the product or service in mind, the more likely it is that the customer is satisfied with the subsequent purchase and use; (2) when a product category is completely new, the basis for developing expectations will be vague and indirect--customer satisfaction will depend on more fundamental needs and actual experiences with the product; (3) the more experience and available information, the more expectations will reflect the actual experience--expectations and experience will be identical and reduced to only one variable. Oliver (1977) found in his empirical study that when disconfirmation has a dominating effect and expectations at the same time are vague, it is mainly characterized by: high-involvement situations; the actual experience is more important than expectations; situations where it is no longer important whether the level of expectation is maintained or not. Model 5 Model 5 is based on the assumption that customers are, to a greater extent, guided by their expectations than their actual experiences. Customers' actual experiences must not be so important that they result in substantial disconfirmation. This will, for instance, be the case when: (1) it is difficult to evaluate actual quality experience since no true objective measure exists; (2) a specific technical knowledge is required to evaluate the quality; (3) it is impossible or very difficult to record the quality (e.g. health-care products, art, computers and long-lasting detergent). Oliver (1980) and Yi (1991) discuss such situations. Buying behaviour and the customer satisfaction process Relevant product category characteristics We believe that the effect of perceived quality on customer satisfaction differs for different product categories and that the satisfaction process is mainly determined by: (1) Price: The product's economical strain on a person's budget. A high price means, other things being equal, that the product charges heavily on the customer's budget and results in a higher financial risk for the customer. (2) Complexity: How difficult it is to evaluate the product's actual quality. It can be difficult if: the product is complex--most often of technical type; objective quality measures are lacking; the product is non-transparent--whether because it demands a special technical knowledge to evaluate the quality or because it is difficult to record the quality. (3) Sign value: How prestigious the product is to the customer in relation to his/her social environment. The customer's status is reflected through the product. Combining product characteristics and customer satisfaction models We can now combine the models with the above-mentioned characteristics. A priori, we believe that: (1) The lower the price, the less influence will expectations have on customer satisfaction. Partially viewed, this means: low price, model 4; high price, models 1, 2 and 3. (2) The lower the product complexity, the less influence expectations will have on customer satisfaction. Partially viewed, this means: low complexity, model 4; high complexity, models 5, 1 and 2. (3) The lower the sign value, the less influence expectations will have. It is the perceived quality that counts--the product must work all right. A partial view means: low sign value, model 4; high sign value, models 1, 2 and 3. Combining the three product characteristics In the next section we want to study the assumptions empirically. For this purpose Table 1 is set up. We combine low and high values of the three different product characteristics, and fill in the cells with relevant product categories that fulfill the characteristics mentioned. Since we assume no three-factor interaction effect between the product characteristics, it will be sufficient for the following analysis to have data from the product categories within the four cells shown in the table. The design is a half fraction of a 23 factorial design. The measurement instrument Expectations, perceived quality and customer satisfaction are seen as unobservable latent variables and, therefore, we need indicators to measure these latent variables. The latent variables were operationalized in the same way as in the Swedish customer satisfaction barometer (SCSB) (Fornell, 1992) and ACSI (Fornell et al., 1996), two well-known national cross-company and cross-industry measurement instruments of customer satisfaction. SCSB and ACSI were launched in 1989 and 1994, respectively, and have been used annually since. Each of the three latent variables was operationalized by three measurement variables (see Table 2). Data collection Data were collected from MSc students at the Aarhus School of Business and the Copenhagen Business School. Six hundred and sixty-two students completed and returned a questionnaire. Responses were made on 10-point scales for all nine-measurement variables. The survey questions were originally drafted in English and translated into Danish. Respondents were screened to identify purchasers of specific product categories within clearly defined time periods: Major durables (bed, personal computer and stereo equipment): purchased and used within the past 3 years. Semi-durables (contact lenses and perfume): purchased and used within the past 3 months. Fast moving consumer goods (cigarettes, batteries and washing powder): purchased and used within the past month. To measure expectations the respondents were asked to remember their expectations about the particular product before they even purchased it. This is a post-purchase measure of prepurchase expectations, which can give statistical and methodological problems (Carman, 1990; Rust et al., 1994, p. 62). Analysis and results Two procedures were used to evaluate the five model structures estimate the models, namely the covariance structure program LISREL 8 (Joreskog & Sorbom, 1993) and a partial least-squares (PLS) method (Fornell & Cha, 1994). We started the data analysis by using LISREL to get a feeling for the model structure within all eight-product categories. Our data did not follow a normal distribution, but rather a negatively skewed distribution, as often seen in customer satisfaction studies. Therefore, we were not able to use the traditional LISREL method based on maximum likelihood. Instead, a LISREL generalized least squares technique with less stringent assumptions was used. LISREL analyses were conducted for each of the eight product categories and all five-model structures were tested. The best model structure for all eight-product categories turned out to be model 4, where only perceived quality affects customer satisfaction. All our cases indicated that expectations was not an explanatory variable, so our hypothesis about expectations, measured in the way we do, is not a driver for customer satisfaction, as is hereby confirmed. As stated earlier, we believe this result is caused by a combination of measurement and methodological problems and the circumstances that expectations simply is not a driver of customer satisfaction. Using the quality-satisfaction model (model 4) as the basic model structure for all eight product categories, PLS analysis was carried out to obtain estimates. PLS is the preferred estimation procedure for customer satisfaction models such as ACSI and SCSB (Johnson et al., 1998, p. 22). For a discussion and comparison of LISREL and PLS see Fornell and Bookstein (1982). PLS estimates the inner relation (the structural model), i.e. the relationship between the latent variables, and the outer relations (the measurement model), i.e. the relationships between the measurement variables and the latent variables. We assume a reflective (outward) measurement model where the measurement variables can be viewed as a reflection of an underlying construct. Table 3 shows PLS results of model 4 for all eight-product categories. R2 is the coefficient of determination in the model, i.e. the proportion of the variation of customer satisfaction that is explained by perceived quality. Four values of R2 have acceptable levels (minimum level of 0.65), and it is remarkable that it is for the high-priced products. On the other hand, the four unacceptable R2 values come from low-priced products. Table 3 also shows the estimated outer coefficients, i.e. weights for each measurement variable associated with the two latent variables. It can be seen that the best indicator for customer satisfaction in all eight cases is CS1, which measures customers' overall satisfaction. This approach is perhaps the most common in practice (Ryan et al., 1995, p. 12). The best indicator for perceived quality is, in four cases, Q2, which measures how well the product fit the customer's requirements, and in three cases Q1, which measures customers' overall evaluation of quality experience. Analysis of variance is used to study the impact of the three different product category characteristics on the explanatory power of the quality-satisfaction model. We examine the impact of these independent characteristics simultaneously. Complexity and sign value are non-significant, whereas the price positively affects the explanatory power (p-value 0.037). This means the more heavily the charges on the customer's budget, the stronger relationship between perceived quality and satisfaction. These results regarding the impact of customer expectations, obtained under experimental conditions, are supported by two Danish applications of a new developed joint European customer satisfaction measurement instrument. Customer satisfaction measurement for Post Denmark The successful experiences of the Swedish and American customer satisfaction indices have inspired recent moves towards creating an ECSI, supported by the European Commission (Directorate General III for Industry), the European Organization for Quality (EOQ) and the European Foundation for Quality Management (EFQM). A pilot study in 1999 is planned in 10 European countries. The authors are responsible for developing and introducing the Danish customer satisfaction index as a national part of ECSI. European experts have developed the ECSI methodology, based on a set of requirements (ECSI Technical Committee, 1998). The basic ECSI model is a structural equation model with unobservable latent variables. The model links customer satisfaction to its determinants and, in turn, to its consequence, namely customer loyalty. The determinants of customer satisfaction are perceived company image, customer expectations, perceived quality and perceived value ('value for money'). Perceived quality is conceptually divided into two elements: 'hard ware', which consists of the quality of the product/service attributes and 'human ware', which represents the associated customer interactive elements in service, i.e. the personal behaviors and atmosphere of the service environment. Main causal relationships are indicated; actually many more points of dependence between the variables can exist. Each of these seven latent variables is operationalized by two to five measurement variables, observed by questions to customers, and the entire system is estimated using PLS. During the autumn of 1998 data were collected for the first estimation of this model in Denmark. In total, approximately 3000 respondents were interviewed about their attitudes towards Post Denmark. Data collection was performed in three different ways in order to study the consequences of different procedures. The methods were: (1) a direct postal survey; (2) a postal survey with pre-notification; and (3) a telephone survey. The difference between (1) and (2) was non-existent, while there was a small bias from the telephone survey, which tended to under-represent higher educated people. Basically, however, the differences were small, and hence the choice of method could be based solely on economical considerations. The estimation of the model, showed that the ECSI structure gives a very good explanation of customer satisfaction. Furthermore, it showed that the proposed split between 'hard ware' and 'human ware' quality was a good idea, since the impact from these two areas is quite different in certain situations. The 'hard ware' elements are called postal service and the 'human ware' elements are called customer interaction. The model deals with all kinds of postal services, parcel delivery, mail and counter services. The ECSI Technical Committee requires that R2 of customer satisfaction should be at least 0.65 (ECSI Technical Committee, 1998, p. 20). Furthermore, a 95% confidence interval for customer satisfaction should not be wider than plus and minus 2 points. The Danish postal model fully lives up to these requirements. Thus, the R2 is 0.79 and the confidence interval is much narrower than plus and minus 2 points due to the very large sample size. When compared to the basic ECSI model we see that there are some slight differences. First, postal service also has a direct effect on loyalty. Second, expectations have only a significant effect on perceived value--not on satisfaction. The indirect impact of expectations on customer satisfaction is low: a one-point increase in expectation index results in a 0.06 X 0.16 = 0.0096 point increase in the satisfaction index (on a 0-100-point scale). This impact is negligible when compared to the other exogenous variables. If we calculate all direct and indirect effects we see that a one-point increase in either perceived image, perceived quality of postal service or customer interaction results in an increase in the satisfaction index of 0.27 point, 0.35 point or 0.29 point, respectively. A very surprising result is the impact of image. Image is by far the most important factor when it comes to the generation of loyalty. This conclusion is very important since competition is going to increase dramatically in the future. Based on the model, the total customer satisfaction for Post Denmark in the private market may be estimated as 63.9. This result is very close to the results obtained in the US, Sweden and Germany. The ECSI model has also been applied to Post Denmark's business market. Based on interviews with 373 business professional customers, we obtained the estimated model. Also, here the ECSI structure gives a very good explanation of customer satisfaction (R2 = 0.78). For our purpose, we find that the impact of expectations is at the same low level as on the private market. Although expectations are significant in the two estimated postal models, its influence on customer satisfaction is negligible compared to the other three exogenous antecedents of satisfaction. Our experience with this first application of the ECSI model has been very good. The model fits well and seems to be sufficiently flexible for different industries. Hence, the model will be applied to other industries during spring 1999. Telecommunication, financial services, supermarkets and various kinds of processed food will be among the industries measured. Conclusion Structural equation modeling is used to estimate and test the process of customer satisfaction formation in eight selected product categories with different combinations of three product category characteristics: price, complexity and sign value. In our cases, it is customers' perceived quality that drives their satisfaction. Customer expectation has no substantive effect on satisfaction. The relationships between perceived quality and satisfaction (the structural model) and the weights of the questionnaire items (the measurement model) are studied across the product categories and the three characteristics. R2 in the structural model has an acceptable level for the high-price products, but an unacceptable level for the low-priced products. This indicates that it is difficult to measure perceived quality for low-priced and low-involvement products by the three survey questions applied. The results regarding the impact of customer expectations, obtained under experimental conditions, are supported by two Danish applications of the ECSI model. Here customer expectations have a negligible effect on customer satisfaction, compared to the other drivers of satisfaction. This holds good of both the private market and the business market. Appendix Table 1. Price versus complexity versus sign value for eight product categories Sign value Price Complexity Low High Low Low Perfume Cigarettes High Washing powder Battery High Low Bed Subscription to Contact lenses High Personal computer Stereo equipment Table 2. Latent variables and measurement variables Legend for chart: A1=Overall expectation of quality E1 A2=How well the product fits the customer's personal requirements E2 A3=How often things would go wrong E3 A4=Overall evaluation of quality experience Q1 A5=How well the product fit the customer's personal requirements Q2 A6=How often things have gone wrong Q3 A7=Overall satisfaction CS1 A8=Confirm/not confirm expectations CS2 A9=Quality versus the customer's ideal product in the category CS3 Latent variable Measurement variable Customer expectations (pre-purchase) A1 A2 A3 Perceived quality (post-purchase) A4 A5 A6 Customer satisfaction A7 A8 A9 Table 3. PLS estimates of the quality-satisfaction model Legend for chart: A1=Product category A2=Perfume A3=Cigarettes A4=Washing powder A5=Battery A6=Bed A7=Contact lenses A8=Personal computer A9=Stereo equipment Weights (outer coefficients) A1 R2 Q1 Q2 Q3 CS1 CS2 CS3 A2 0.61 0.37 0.48 0.30 0.45 0.25 0.42 A3 0.36 0.48 0.56 0.20 0.86 0.18 0.21 A4 0.29 0.05 0.60 0.70 0.58 0.30 0.26 A5 0.46 0.42 0.49 0.31 0.55 0.35 0.36 A6 0.66 0.41 0.41 0.30 0.47 0.30 0.36 A7 0.74 0.43 0.42 0.33 0.43 0.36 0.33 A8 0.68 0.40 0.39 0.36 0.49 0.34 0.35 A9 0.67 0.44 0.35 0.36 0.46 0.34 0.38 References Anderson, E.W. & Sullivan, V.W. 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