EVALUATING CUSTOMER SATISFACTION: A CONTINGENCY MODEL APPROACH

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1 Tourism Analysis, Vol. 6 pp /02 $ Printed in the USA. All rights reserved. Copyright 2002 Cognizant Comm. Corp. EVALUATING CUSTOMER SATISFACTION: A CONTINGENCY MODEL APPROACH DAN TOY,* DEBORAH KERSTETTER, and ROBIN RAGER *Department of Marketing, California State University, Chico, 307 Tehama Hall - 051, Chico, CA Department of Leisure Studies, The Pennsylvania State University, 201 Mateer, University Park, PA Department of Health Studies, Texas Woman s University, P.O. Box , Denton, TX Increased competition between leisure service businesses has forced managers to place greater emphasis on understanding and satisfying their customers needs. Traditionally a modified version of the Parasuraman, Zeithaml, and Berry service quality model has been used to assess how well businesses are meeting their customers needs. This approach is flawed, however, because it cannot explain the variability of outcomes. This study demonstrates the considerable flexibility and power of using a contingency approach to leisure service performance/satisfaction modeling. The results indicate that it is an effective research methodology for understanding the determinants of customer satisfaction for targeted clientele and a basis for positioning or repositioning the service mix to meet customers needs. Contingency modeling Customer satisfaction Leisure services In the last decade, the number and type of service businesses, especially those in the American leisure marketplace, have undergone substantial change. In fact, some have argued that America has undergone a service revolution (see Baum, 1998; Fisk, Brown, & Bitner, 1993; Gummeson, 1991; Hudson & Shepard, 1998; Laws, 2000), focused primarily around developing services that respond to customers needs. The ultimate goal of this customer-centric strategy is to generate competitive advantage by providing greater value leading to increased levels of customer satisfaction. A customer-oriented marketing program in the leisure service industry requires that businesses systematically monitor customer satisfaction (Backman & Veldkamp, 1995; Hamilton, Crompton, & More, 1991; MacKay & Crompton, 1990; Wicks & Fesenmaier, 1993; Wright, Duray, & Goodale, 1992). In doing so they must: 1) identify the important determinants of service success, 2) assess the relative Address correspondence to Deborah Kerstetter, Associate Professor of Leisure Studies, The Pennsylvania State University, 201 Mateer, University Park, PA Tel: (814) ; debk@psu.edu 99

2 100 TOY, KERSTETTER, AND RAGER importance of each service attribute, 3) determine important customer segments, and 4) measure the linkage between the determinants of success and overall satisfaction for all customers as well as relevant customer subgroups. Although there are a number of methodologies for conducting customer satisfaction research, one of the most useful is contingency modeling. The primary objective of this article is to demonstrate the value of using contingency modeling in a leisure service setting. Specifically, this article will highlight how contingency modeling can be used to model customer segments evaluations of service performance and the relationship of these evaluations to overall satisfaction. Service Quality and Satisfaction Writers in the popular press often use the terms quality and satisfaction interchangeably. However, researchers have made an important distinction between the two constructs in terms of both outcomes and causes. For example, individuals perception of quality is represented by their inference about a [services] overall excellence or superiority, based on a rational assessment of characteristics and an affective judgement (Gyimothi, 2000, p. 34). In the context of leisure services, Manning (1986) defines quality as the degree to which opportunities satisfy the motivations for which they are designed (p. 120). Perceived quality does not exist in a vacuum, however. It is closely aligned with satisfaction (Iacobbucci, Ostrom, & Gratson, 1995; Johnson & Mathews, 1997; Taylor & Baker, 1994). According to Oliver (1997), satisfaction is a judgement that a product or service feature, or the product or service itself, provides a pleasurable level of consumption-related fulfillment (p. 13). It may also be considered the psychological outcome which emerges from experiencing the service (MacKay & Crompton, 1990, p. 48). In the leisure service industry much of the research examining service quality has been built upon Parasuraman, Zeithaml, and Berry s (1988) SERVQUAL model. Parasuraman et al. noted, however, that the SERVQUAL model provides only a framework and should be modified to meet the needs of specific services. Thus, a small cadre of researchers (see Crompton, MacKay, & Fesenmaier, 1991; Hamilton et al., 1991; Wright et al., 1992) and others have attempted to reoperationalize items in the model to more effectively represent their service setting. Specifically, MacKay and Crompton (1988) proposed REQUAL for studying traditional recreation services; Stevens, Knutson, and Patton (1995) created DINESERV as a model for measuring dining services; and Knutson, Stevens, Wullaert, and Patton (1991) acknowledged differences in lodging services through LODGSERV. To study customers use of services within the context of a country club setting, however, we chose to use a new model proposed by Zeithaml and Bitner (2000, pp ). A graphical representation of this model is presented in Figure 1. Service quality is specified as a function of the quality of specific service dimensions (e.g., reliability, responsiveness, assurance, empathy, tangibles). Overall service quality in combination with product quality and price (where applicable) determine overall satisfaction. Customers overall satisfaction with a service, especially leisure services, can also be affected by situational and personal factors (e.g., past experience, loyalty) (Backman & Veldkamp, 1995; Hamilton et al., 1991). In this study, we investigate the relationship between several of the components of service quality (i.e., price, age, level of use) and overall customer satisfaction. The Study Setting A private country club was experiencing problems with declining membership. The club s board of directors felt that a number of factors were contributing to this problem, including: 1) changes in Figure 1. Service quality and satisfaction.

3 CONTINGENCY MODEL FOR CUSTOMER SATISFACTION 101 the demographic characteristics of the club s clientele, 2) recent price increases by the club, 3) the service mix provided by the club, and 4) loss of younger members to other private and public club competitors. The board felt that the club should be repositioned but was unsure of the factors leading to satisfaction (or dissatisfaction) with the club s performance. In addition, the board wanted to know how these factors might differ in importance for separate customer segments. The board was particularly interested in two different segmentation bases: one representing the age of the customer, and the other the degree to which customers used the facility for playing golf. The research that is described next was designed to address these important marketing issues from the standpoint of modeling satisfaction with leisure services. Methodology Establishment of Service Attributes The first step was to establish which service attributes the club s customers considered most important. To obtain a list of potential service attributes we used three sources: a review of related research (the SERVQUAL instrument in particular see Parasuraman et al., 1988), leisure service and vertical trade literature, and input from professionals in the field, including personnel from the club. The resulting list of service attributes, clustered into 10 attribute groupings, was used for directing three separate focus group discussions with club members (for examples of how focus group methods can be used to determine salient service attributes, see Alpert, 1971; Calder, 1976; Dupont, 1976; Fern, 1982). Each focus group included 10 club members. Special care was given to the composition of each group so that important segments of the club (i.e., age, gender, level of play) were adequately represented. The major orientation of the sessions was to determine, in the members own words, the important attributes of the club and whether the quality of these attributes had a major impact on overall satisfaction with the club. In addition to the traditional qualitative aspects of the focus group, a quantitative task was included at the end of each session, which required participants to evaluate a comprehensive list of attributes on sevenpoint semantic differential scales. The results of the focus group sessions indicated that five attributes were most related to members overall satisfaction with the club. These included: 1) the difficulty (i.e., challenge) of the golf course (SERVQUAL dimension = tangibles); 2) the condition of the golf course (SERVQUAL dimension = tangibles); 3) the management and operation of the club (SERVQUAL dimension = assurance and reliability); 4) the atmosphere of the club (SERVQUAL dimension = empathy; and 5) the cost of membership at the club compared to benefits received (SERVQUAL dimension = relative price). Measures The members evaluation of club performance (quality) on each of the five attributes described above was assessed using a number of different semantic differential scales, as suggested by Andreasan (1977), Martilla and James (1977), and Duray and Crompton (1984) (see Table 1). The five attributes represent the independent variables in subsequent analyses. The dependent variable in the study was members overall satisfaction with the club (see Table 1). The two segmentation variables (corresponding to personal and situational factors in Fig. 1) were age and level of play. The selection of these two variables, and the levels of the variables, was based on discussions with club members and the club management and owners. As a result of our interactions with customers and management, subjects were divided into three age groups [21 to 39 years old (n = 53), 40 to 59 years old (n = 57), and 60 and older (n = 98)] and classified into three segments based on self-reported level of play [no golf (n = 83), golf one or two times per week (n = 53), and golf more than three times per week (n = 72)]. Subjects and Questionnaire Administration The survey instrument was pretested with 10 club members and minor changes were made in the final instrument based on their feedback. The survey was then mailed to each of the club s 368 members. A total of 208 questionnaires were received (after one reminder follow-up message) for a response rate of 57%. The sample was tested for how well it matched the club s membership demographics. No

4 102 TOY, KERSTETTER, AND RAGER Table 1 Measures of Service Performance and Overall Service Satisfaction The difficulty/challenge of the Very easy Neither easy Very golf course at your club or difficult difficult 2. The condition/grooming of the Poor Excellent golf course at your club 3. The management of your club Poor Excellent 4. The overall atmosphere at Very Neither friendly Very your club unfriendly or unfriendly friendly 5. The cost of the membership Low cost High cost at the club, compared to the compared compared benefits you receive to benefits to benefits 6. Your overall satisfaction with Very Moderately Slightly Neither satisfied Slightly Moderately Very the club dissatisfied dissatisfied dissatisfied or dissatisfied satisfied satisfied satisfied significant differences were found between the sample and the club population s characteristics. Contingency Modeling Approach The concept of contingent relationships and contingency modeling has been discussed in several disciplines (see Collins, Milliron, & Toy, 1992; Gujarati, 1970a, 1970b; Hayes, 1977; Hofer, 1975; Lawrence & Lorsch, 1967; Lilien & Weinstein, 1984; Ruekert, Walker, & Roering, 1985; Tellis & Fornell, 1988; Wright, 1995). Although no formal definition of contingency models exists, Hofer (1975) described the general premise of the technique in the business strategy literature. Hofer argued that universal models of complex phenomenon are inadequate for explaining the variability of outcomes. In particular, when applied to business strategies, Hofer stated, unless one is willing to admit the possibility that there exists some strategy or set of strategies that are optimal for all businesses no matter what their resources and no matter what environmental factors they face an assumption that is inconsistent with all research studies on business strategies to date any theory of business strategy must be a contingency theory. (p. 785) Thus, the key to greater understanding of complex phenomenon (such as customer satisfaction one component of business strategy) lies in the ability to establish contingency factors that allow for more precise relations between predictors and criteria. In our example of applying the satisfaction model to a leisure service setting, the contingency factors are the individual difference and situational variables included in the modeling process. While a definitive procedure has not yet been established for contingency model testing, the following issues must be addressed: 1) the association between dependent and independent variables in the model without considering contingency factor(s); 2) whether the inclusion of the contingency variable(s) in the model significantly increases the predictive power of the model; 3) if significant changes occur in the slope or intercept coefficients for different levels of the contingency factor; and 4) the structural relationships between the dependent and independent variables for each level of the contingency factor. In the analysis section that follows, Gujarati s (1970a, 1970b) general approach for testing contingency relationships is extended and refined to specifically address these four issues. Analysis The analysis procedures were based on the premise that segments of the club s membership would exhibit different response functions relating attribute performance and overall club satisfaction. Specifically, the characteristics that are considered important in determining satisfaction are contingent on the age of the member and their level of play. Note that there are two contingency factors examined in this study. In the discussions that follow, only

5 CONTINGENCY MODEL FOR CUSTOMER SATISFACTION 103 the procedures for examining the age factor are described. The same analytical steps were incorporated for examining level of play as a contingency factor. The first step in the analysis involved a regression of overall club satisfaction on the five performance characteristics without including the age contingency factor. Y = B 0 + B 1 X 1 + B 2 X 2 + B 3 X 3 + B 4 X 4 + B 5 X 5 (1) where Y is a measure of degree of satisfaction with the club; X 1 to X 5 are the five performance evaluation measures. Then, each level of the age contingency factor was coded as a 0,1 dummy variable. The contingency factor of age in this research consisted of three groups (21 39, 40 59, 60 and older). A dummy variable was assigned for each contingency level so that D 1 = 1 if age = and D 1 = 0 if age not equal to 21 39, etc. This resulted in three contingency variables, each representing a level of the contingency factor (D 1,, and ). In this study the youngest age group (D l ) was identified as the base condition for subsequent regression analyses. The base represents the comparison level of the contingency factor for establishing differences in partial intercept and slope coefficients. The next step was to create interaction variables by multiplying each independent variable by each contingency level. In our model there were five independent variables and three contingency levels, so that 15 interaction variables were formed (i.e., D 1 X 1, D 1 X 2, etc.). In the model presented below, the base condition (D l ) is not represented by interaction or intercept values in the regression equation. In fact, only CL-1 contingency levels can be included in the model where CL = contingency factor level. If all levels are included, there will be linear dependency in the predictor set and the equation cannot be estimated. The final regression equation that was tested is as follows: Y = B 0 + B 1 X 1 + B 2 X 2 + B 3 X 3 + B 4 X 4 + (2) B 5 X 5 + B 6 + B 7 X 1 + B 8 X 2 + B 9 X 3 + B 10 X 4 + B 11 X 5 + B 12 + B 13 X 1 + B 14 X 2 + B 15 X 3 + B 16 X 4 + B 17 X 5 where X 1 to X 5 are the five independent variables; and are dummy variables for age groups and over 60; X j D k is interaction effects for contingency levels; B i is ordinary least squares regression coefficients. In Equation (2) the coefficients for the noninteraction variables (B 1 through B 5 ) are interpreted as the direct relationship between the performance variables and overall service satisfaction for the base level of the age contingency factor. Likewise, B 0 is interpreted as the intercept coefficient for the age group. This means that coefficients B 0 through B 5 in Equation (1) are not interpreted the same as B 0 through B 5 in Equation (2). Equation (1) coefficients represent the relationships Y = f (B 0 B 5 ) for the whole sample, whereas Equation (2) coefficients depict the Y = f (B 0 B 5 ) for the yearold segment only. The other coefficients in the model represent changes in the regression slope (e.g., B 13 ) or the intercept (e.g., B 12 ) from the base relationship. This is conceptually equivalent to running a Chow test (Chow, 1960) on the differences in regression coefficients between two equations. However, it has certain efficiency benefits over the Chow test that makes it a more desirable technique (Gujarati, 1970a). An incremental F-test was calculated to determine whether the age contingency factor added significantly to the explanatory power of Equation (1). F =[(R 2 cm R2 )/(k k )]/ (3) m cm m [(l R 2 )/(N k 1)] cm cm with (k cm k m )/(N k cm 1) degrees of freedom where R 2 is the squared multiple correlation coefficient for the contingency model (2); R 2 is the cm m squared multiple correlation coefficient for the noncontingency model (1); k cm is parameters estimated in the contingency model; k m is parameters estimated in the noncontingency model; N is total sample size. In terms of hypothesis testing, the F-statistic above tests the following: HO: B i = 0 for all i > 5 (4) HA: B i 0 for all i >5 In other words, the F-statistic tests for significant differences in intercept or slope between the base contingency level and other contingency levels and determines whether this difference adds significantly

6 104 TOY, KERSTETTER, AND RAGER to the explanatory power of the model. Note that in some cases researchers may be interested in whether the differences in contingency and noncontingency models are due to changes in the intercepts of the two equations, changes in slope estimates, or changes in both intercepts and slopes. For details on these tests, see Gujarati (1970a, 1970b). The final step in the contingency model evaluation required combining appropriate coefficients from the interaction Equation (2) to examine the relationship between satisfaction and performance variables for each contingency level. For example, the relationship between Y and X 1 for year olds was calculated by adding the coefficients for the base case (B l ) and the interaction effect for level 2 of the contingency factor (B 7 ). This coefficient, when divided by its appropriate standard error, is distributed as a t-statistic and can be tested for significance like any regression coefficient: regression coefficient = B 1 + B 7 (5) standard error of B 1 + B 7 = [var(b 1 ) + var(b 7 ) + 2cov(B 1,B 7 )] 1/2 Similar tests were calculated for all contingency factor levels (interaction variables) by combining the appropriate coefficients for the point estimates and the variances and covariances for the standard errors. Note that the appropriate tests for the base case were automatically generated by Equation (2). That is, the test of the relationship between Y and X, for the age group, is given by B 1 in Equation (2). (Note: as mentioned above, the same analysis was repeated for level of play.) Results and Interpretation Noncontingency Model The first model examined was the regression of satisfaction on the five performance measures based on Equation (1). The results are presented in Table 2. Each of the five performance variables proved to be significant predictors of satisfaction with an R 2 of 0.44 for the model. These results indicate that improvement in any of the service attributes would contribute to an increased overall satisfaction with the club. What is not clear from these results is whether such improvements would benefit all members equally. Table 2 Noncontingency Model Variable Description Coefficients Probability Difficulty of golf course < Condition of golf course < Management/operation of club < Atmosphere at club < Cost/benefit of club membership < Intercept Adjusted R 2 = 0.441; F(5, 197) = 29.13, p < Age Group Contingency Model A contingency analysis was performed to assess the effect of age on the relationship between satisfaction and performance on the five service attributes. The R 2 value for the contingency model based on Equation (2) was The incremental F- test to assess the significance of adding the contingency factor [see Equation (3)] proved significant, F(12, 185) = 6.795, p < Thus, consideration of the age contingency factor added significantly to the explanatory power of the noncontingency model that did not include interactions. Table 3 presents the tests for determining if coefficients for the 40 to 59 and 60 and above age groups differ from those for the base case (21 to 39 year olds). Note that this test does not indicate whether younger or older members feel that golf course difficulty is significantly related to overall satisfaction. Rather, it examines whether or not the relationship between golf course difficulty and overall satisfaction with the club are the same for these two groups. For example, this table shows that older members feel the difficulty of the golf course was less important than younger members in terms of their overall satisfaction with the club. This is manifested by the coefficient ( 0.345, p < 0.05) representing the change in the 60 and above age level compared to the year-old base condition. In other words, this coefficient tests the hypothesis that Base 21 to 39 = Base 60 and older for golf course difficulty. The null hypothesis that these two coefficients are equal is rejected in this case. Other significant results from Table 3 may be interpreted in a similar fashion. The actual regression coefficients for each level of the age contingency factor were calculated by

7 CONTINGENCY MODEL FOR CUSTOMER SATISFACTION 105 Table 3 Age Group Contingency Model: Raw Regression Coefficients Variable Description Age (21 39) a Age (40 59) b Age (60 and Above) b Difficulty of golf course * Condition of golf course Management/operation of club ** Atmosphere at club * 0.317*** Cost/benefit of club membership * Intercept a This column of regression coefficients represents the actual relationship between the criterion and predictor variables for subjects in the age group. b These two columns of regression coefficients represent changes in the slopes or intercepts between the age group base condition and either of the other two age groups. *p < 0.05, **p < 0.01, ***p < combining the appropriate coefficients from Table 3. These results are shown in Table 4. Note that one of the service attributes was important to all age groups (management/operation of the club), while others were relevant only to certain club segments. For example, the difficulty of the golf course was not significantly related to satisfaction for the older members, but was for the two younger segments. These regression results were augmented by a oneway analysis of variance with age segments as the independent variable and overall club satisfaction as the criterion measure. The F-test was significant for this analysis, F(2, 205) = 3.301, p < 0.05, indicating differences between the three age groups in terms of their overall satisfaction with the club. This analysis revealed that the younger groups were less satisfied with the club than their older counterparts. This result is consistent with a priori information that younger groups had the highest attrition rate. Level of Use Contingency Model A second contingency factor, level of use, was tested in a similar fashion. (Note: although not explored in this study, it is possible to look at the interaction of two contingency variables in terms of their combined impact on the relationship between Y and several quantitative measures. This is accomplished by setting up a dummy variable for each combination of levels for the contingency factors in ques- Table 4 Age Group Contingency Model: Actual Regression Coefficients Variable Description Age (21 39) Age (40 59) Age (60 and Above) Difficulty of golf course 0.447** 0.257* Condition of golf course m Management/operation of club 0.530**** 0.245** 0.351**** Atmosphere at club ** 0.382**** Cost/benefit of club membership m 0.234**** Intercept Each of the columns of regression coefficients in this table represent the direct relationship between predictor and criterion. Values for the age group are the same as those in Table 3. Values in the Age (40 59) and Age (60 and Above) columns represent the combination of the base case results in Table 3 plus changes in the base condition from Table 3. For example, Age (40 59)/Difficulty of golf course (0.257) = (0.447) + ( 0.190) from Table 3. Adjusted R 2 = 0.612; F(17, 185) = , p < m p < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001, ****p <

8 106 TOY, KERSTETTER, AND RAGER Table 5 Golf Course Use Contingency Model: Raw Regression Coefficients Variable Description Nongolfers a Golf 1 2 Times/Week b Golf 3+ Times/Week b Difficulty of golf course Condition of golf course * Management/operation of club Atmosphere at club Cost/benefit of club membership Intercept a This column of regression coefficients represents the actual relationship between the criterion and predictor variables for subjects in the nongolfer group. b These two columns of regression coefficients represent changes in the slopes or intercepts between the nongolfer group base condition and either of the other two golf usage categories. *p < tion. In the present analysis, this would require the formation of nine contingency levels. For one example of such an approach see Collins et al., 1992.) The base case for these analyses was the nongolfing segment. The results are presented in Tables 5 and 6. Segmenting by level of play provided a significant increase in the explanatory power over the noncontingency model presented in Table 2, F(12, 185) = 5.402, p < As would be expected, these analyses showed that members who golf more often placed greater emphasis on the golf courserelated attributes. The nongolfers perceived the management of the club and club atmosphere to be more instrumental in impacting their overall satisfaction. A one-way ANOVA with satisfaction as the dependent variable showed that as level of play increased, the members were more satisfied with the club s performance, F(2, 205) = 3.984, p < Summary and Conclusions Overall, the results from the contingency models provide greater insight into possible repositioning strategies for the club than could be determined from a straightforward regression approach. While the noncontingency model indicated that all of the service attributes were important in determining service satisfaction, the contingency models were able to identify which factors were important to designated subgroups. For example, several marketing tactics for the club are suggested by the results from the age-contin- Table 6 Golf Course Use Contingency Model: Actual Regression Coefficients Variable Description Nongolfers Golf 1 2 Times/Week Golf 3+ Times/Week Difficulty of golf course * 0.211* Condition of golf course *** Management/operation of club 0.429**** 0.371*** 0.304*** Atmosphere at club 0.272*** 0.269** 0.330**** Cost/benefit of club membership 0.164* *** Intercept Each of the columns of regression coefficients in this table represent the direct relationship between predictor and criterion. Values for the nongolfer group are the same as those in Table 5. Values in the Golf 1 2 Times/Week and Golf 3+ Times/Week columns represent the combination of the base case results in Table 5 plus changes in the base condition from Table 5. For example, Golf 1 2 Times/Week/ Difficulty of golf course (0.201) = (0.149) + (0.052) from Table 5. Adjusted R 2 = 0.586; F(17, 185) = , p = < *p < 0.05, **p < 0.01, ***p < 0.001, ****p <

9 CONTINGENCY MODEL FOR CUSTOMER SATISFACTION 107 gency analysis. First, and most obvious, is the importance of improved management/operation of the club. The overall impact of addressing this concern should be substantial because it was rated as instrumental to overall satisfaction by each age segment. It also appears that the atmosphere and cost of the club are relevant issues to older clientele. Like the management factor described above, changes in how members are treated (especially senior members) by staff and a realization that some older members have fixed incomes and are unduly taxed by increases in fees are important marketing considerations. In addition, the difficulty of the golf course was strongly related to overall satisfaction for the youngest groups. However, since the course was already considered to be challenging, adequately maintaining the club s already rigorous course should have a positive impact on these members. 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