A Bayesian Approach for Analyzing the Services of Banking Institutions

INTRODUCTION Banking institutions today must create a perception of uniqueness in the mind of the customer to gain an advantage in the marketplace. The evolving customer base brought about by geographic, demographic, and life-style variances must be monitored to maintain and enhance market position. Of utmost importance is to estimate the changing nature and scope of financial markets from a customer-directed focus. The interrelationships of variables defining the antecedents and also the consequences of customer satisfaction have been studied extensively in the consumer research literature (e.g., Anderson and Sullivan 1993; Bearden and Teel 1983; Bolton and Drew 1991a, 1991b; Cardozo 1965; Churchill and Suprenant 1982; Cronin and Taylor 1992; La Barbera and Mazursky 1983; 6 and Peat 1979; Oliva, Oliver, and MacMillan 1992; Oliver 1977, 1980; Oliver and Bearden 1985; Oliver and DeSarbo 1988; Oliver and Swan 1989; Tse and Wilton 1988; Westbrook 1981; Yi 1990). However, there appears to be conflicting evidence as to the nature of the linkages between the antecedents and consequences of satisfaction. For example, Cronin and Taylor (1992) report structural modeling results which indicate perceived service quality is an antecedent to satisfaction. However, Bolton and Drew (1991a, 1991b) give conceptual representations where the satisfaction construct is an antecedent to a service quality construct. Yi's (1990) review reports some of the conflicting findings that exist in different studies. One of the most important directions for future research is to shed more light on which paradigm may best model customer satisfaction judgments in various applications (Erevelles and Leavitt 1992). The use of methods that incorporate structural information in an adaptive manner, using or capitalizing on it to the extent that observed data are consistent with that information, may be especially useful in modeling customers' preferences. Prior structural information plays an important role in this context, even when relevant prior information is vague in form. In relation to structural equation systems in the literature which assume a priori knowledge concerning the linkages between constructs, adaptive methods are less likely to suggest knowledge where it may not exist, especially in situations where the investigation is genuinely exploratory. This is consistent with the recommendation by many researchers to use methods that account directly for the conditions that characterize a specific problem (cf. Laughlin 1986). The purpose of this study is to present a Bayesian Structural Regression (BSR) paradigm for modeling customers' perceptions of banking services and identifying the important determinants of preference for a particular banking institution. Unlike previous research in the literature, this study uses adaptive structural methods to model customer preference data. These methods are based on conjugate Bayesian theory discussed by Dempster (1969) and made operational by Chen (1979) using the EM method (Dempster, Laird, and Rubin 1977). The Bayesian approach provides a mechanism for incorporating prior structural information into covariance estimation. This information can be either vague or specific and is used only to the extent that it reflects worthwhile information about as many interrelationships among variables as possible. Exploratory common factor models are used here to represent vague prior knowledge about covariance structure. It will be shown below that common factor models are particularly useful in applications where there are substantial measurement errors in the variables. The current situation in the banking business is one of increased complexity in terms of customer, distribution, and product (Kimball and Gregor 1995). The industry can be characterized by changes in customers' preferences and rising competition among banks and between banks and nonbank service providers. Customer satisfaction with the services of banks is a moving target; banks must continually monitor the rapidly changing marketplace and strive to understand and become more responsive to their customers' needs and preferences (Chakravarty, Feinberg, and Widdows 1995; Glassman 1995). …