Latent Growth Curve Modeling of the Relationships Among Revenue, Loyalty, and Customer Satisfaction By Generalized Structured Component Analysis (Gsca)

EXTENDED ABSTRACT - Many practitioners and academics alike presume that satisfaction leads to loyalty and because loyal consumers are less costly to do business with, revenues increase; and this, in turn, leads to higher profits (Yeung and Ennew 2001). Despite the intuitive appeal of this claim, results from past studies on the effects of satisfaction on performance have tended to be mixed and often conflicting. Bernhardt et al. (1999) attribute this phenomenon to a reliance on cross-sectional data (e.g., Rust and Zahorik 1993; Rust, Zahorik, and Keiningham 1995; for notable exceptions, see Anderson, Fornell, and Lehmann 1994; Bernhardt et al. 1999). The primary purpose of this paper is to empirically demonstrate systematic links between customer satisfaction, loyalty, and annual revenue in a longitudinal and causal manner.

Citation:

Heungsun Hwang, Youngchan Kim, and Marc A. Tomiuk (2005) ,"Latent Growth Curve Modeling of the Relationships Among Revenue, Loyalty, and Customer Satisfaction By Generalized Structured Component Analysis (Gsca)", in AP - Asia Pacific Advances in Consumer Research Volume 6, eds. Yong-Uon Ha and Youjae Yi, Duluth, MN : Association for Consumer Research, Pages: 215-217.

Asia Pacific Advances in Consumer Research Volume 6, 2005      Pages 215-217

LATENT GROWTH CURVE MODELING OF THE RELATIONSHIPS AMONG REVENUE, LOYALTY, AND CUSTOMER SATISFACTION BY GENERALIZED STRUCTURED COMPONENT ANALYSIS (GSCA)

Heungsun Hwang, HEC Montreal, Canada

Youngchan Kim, Yonsei University, Korea

Marc A. Tomiuk, HEC Montreal, Canada

EXTENDED ABSTRACT -

Many practitioners and academics alike presume that satisfaction leads to loyalty and because loyal consumers are less costly to do business with, revenues increase; and this, in turn, leads to higher profits (Yeung and Ennew 2001). Despite the intuitive appeal of this claim, results from past studies on the effects of satisfaction on performance have tended to be mixed and often conflicting. Bernhardt et al. (1999) attribute this phenomenon to a reliance on cross-sectional data (e.g., Rust and Zahorik 1993; Rust, Zahorik, and Keiningham 1995; for notable exceptions, see Anderson, Fornell, and Lehmann 1994; Bernhardt et al. 1999). The primary purpose of this paper is to empirically demonstrate systematic links between customer satisfaction, loyalty, and annual revenue in a longitudinal and causal manner.

In essence, the proposed model hypothesized an indirect link between satisfaction and revenue with loyalty as a mediator of this relationship. Five manifest variables measured across 83 US companies were used: Customer satisfaction in 1994 (CS_94), loyalty in 1994 (Loyalty_94), as well as three measures of annual revenue from 1994 to 1996 (Rev_94, Rev_95, and Rev_96). Customer satisfaction and loyalty scores for the 83 companies were measured on the basis of the American Customer Satisfaction Index (Fornell et al. 1996).

Latent growth curve modeling (Meredith and Tisak 190) was applied to this data. The model was first estimated under a covariance structure framework. However, a Heywood case appeared after estimation (see Bollen, 1989) and rendered respecification problematic. In order to avoid the improper solution encountered in the previous analysis, the use of Partial Least Squares (PLS, Wold 1966, 1973) was entertained. When compared to covariance structure analysis, PLS estimation has been presented as the method of choice for predictive purposes (see Joreskog and Wold 1982). It has been also strongly recommended as a method for estimating Consumer Satisfaction Index (CSI) models because it is not subjected to the strict assumptions about data which underlie the use of covariance structure analysis (Fornell 1992; Fornell and Cha 1994). The major limitation of PLS lies perhaps in that it does not solve a global optimization problem for parameter estimation (Fornell and Bookstein 1982; Joreskog and Wold 1982). This means that there exists no criterion consistently minimized or maximized to determine the estimates of model parameters. The lack of a global optimization criterion makes it difficult to evaluate the overall goodness of fit of the specified model. On the other hand, one of the principal objectives of using latent growth curve models is to choose an optimal temporal pattern of change on a longitudinal variable (McArdle and Bell 2000). To decide on the optimal pattern of change over time, it is necessary to look into the overall goodness of fit of the specified latent growth curve model. This also allows for comparisons among competing models involving different temporal patterns. Thus, PLS may not be an attractive alternative for latent curve modeling because of the difficulty of assessing overall model fit.

Next, it was decided to estimate the model with Generalized Structured Component Analysis (GSCA) (Hwang and Takane in press), a recently proposed method for path analysis with latent variables. GSCA was developed as an alternative to PLS. It defines latent variables as linear combinations of observed variables as in PLS. However, unlike PLS, it provides a global least squares optimization criterion, which is consistently minimized to obtain parameter estimates. The method thus enables the calculation of an overall measure of model fit called EV (Explained Variance) while fully maintaining all the advantages of PLS such as less restricted distributional assumptions, no improper solutions, and unique latent score estimates. As such, GSCA was deemed a suitable alternative to both covariance structure analysis and partial least squares for fitting the specified latent curve model.

GSCA estimation revealed that the specified model fitted the data quite well. Nevertheless, for a more rigorous model evaluation procedure, the specified model was compared to two alternate models: One assumed no time-specific trend or stability, and the other assumed a quadratic trend of change across the three measures of annual revenue. In both alternate models, loyalty had direct effects on all temporal patterns in annual revenue and customer satisfaction had a direct effect on loyalty. After an examination of the goodness-of-fit indices, it was difficult to ascertain whether the linear-trend model was most appropriate because the two alternate models also fitted the data well. Hence, further evaluation appeared necessary so as to ascertain whether the three models were significantly different with respect to their goodness of fit.

Next, the mean differences in EV, the goodness of fit measure, were compared between the fitted models. EV is an absolute index that directly provides certain information about the closeness between the data and a hypothesized model; and this enables comparisons. It was found that there was a significant mean difference between the stability and linear-trend models while there was no significant mean difference between the linear-trend and the quadratic-trend models. This indicated that the linear-trend model (i.e. the model originally proposed) provided a significantly better fit than the stability model while providing essentially the same fit as the quadratic-trend model. Therefore, it was deemed prudent and reasonable to select the linear-trend model as the final model for the data.

In sum, the latent growth curve model estimated by GSCA demonstrated that satisfaction positively impacts financial performance (annual revenue) over time through loyalty. In fact, loyalty appears to play a pivotal role in the model and it is clear that both satisfaction and loyalty are closely related as indicated in Oliver (1999).

To conclude, this study rejoins a few others that have used longitudinal data in an effort to demonstrate the impact of satisfaction on economic and financial performance measures. However, some unique contributions delineate it from others. In particular, a causal approach (latent curve modeling) was adopted here and this appears to have finally provided robust and convincing evidence for the widely held, but seemingly anecdotal, beliefs of practitioners and academics. Moreover, the recently proposed method of GSCA (Hwang and Takane, in press) did a good job at circumventing some of the problems associated with the analysis of covariance structures as well as PLS.

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