Understanding the Factor Structure of Customer Satisfaction in Business Markets

Although creating value for the customer has long been commonly recognized as a pivotal determinant of long business success, the way it is realized has undergone important changes in business-to-business contexts in recent years. In fact, in the past decade, many business customers have decided to forge closer relationships with fewer suppliers, who in turn tend to serve their customers in a broader logic of “business solution” (Capon, 2001). It follows an increase in the breath and depth of the supplier-buyer relationships, that have important implications on the value requirements made by the customers in such a business context. An important consequence of these changes has been the broadening of the dimensions over which the customers make their assessment about the value delivered by their suppliers. As the dimensions of value increase, designing the value proposition with attributes that would maximize the value perception and, consequently, the customer satisfaction gets critical. Traditionally, multiple-regression models have been employed to identify the key attributes into which managers should invest resources to improve customer satisfaction (e.g., Bolton and Drew, 1991; Wittink and Bayer, 1994). These well accepted, “key drivers” models of customer satisfaction are based on the assumption that attribute-level performance and overall satisfaction are linked through a linear and symmetric relationship. As key attribute performance scores increase (decrease), satisfaction increases (decreases) proportionally. In this vein, satisfaction and dissatisfaction are thought of as two sides of the same coin. An increasing body of research has been revealing the existence of a nonlinear and asymmetric response of satisfaction to attribute-level performance (Kano, 1984, Cadotte and Turgeon, 1988a , 1988b; Johnston, 1995; Matzler, Hinterhuber, Bailom and Sauerwein, 1996; Vavra, 1997; Mittal, Ross and Baldasare, 1998) and documenting the serious danger of misallocation of resources resulting from viewing the world through a linear and symmetric lens (Anderson and Mittal, 2000). This literature has shown that while some attributes are relatively important in determining satisfaction, others are not critical to consumer satisfaction but are related to dissatisfaction when performance on them is unsatisfactory. The identification of these satisfier and dissatisfier attributes implicitly suggests that satisfaction and dissatisfaction are not opposites on a single factor continuum, but they represent distinct factors of the customer satisfaction construct. The development of viable satisfaction measurement procedures for identifying the factor structure of customer satisfaction is paramount to correctly prioritize the resource allocation bound to improve the attribute performance (Mittal, Ross and Baldasare, 1998; Anderson and Mittal, 2000; Busacca and Padula, 2005). Yet, although widely recognized, this “factor theory” of customer satisfaction still needs to be further tested and corroborated, and the methodology to identify the factor structure of customer satisfaction still need to be validated. This study realizes an empirical test of this theory based on customer satisfaction data collected from a firm operating in a business-to-business context. The purpose of this study is threefold. First, it shows empirically and discuss the limitations of the traditional “key drivers” analysis, demonstrating that the traditional Importance-Performance Analysis (Martilla and James, 1977) is misleading based on the predictions of the three factor theory of customer satisfaction. Second, it shows empirically and discuss the limitations of the Importance-Grid proposed by Vavra (1997) to identify the three satisfaction factors. Third, it tests empirically the asymmetric and non linear relationships between attribute-level performance and overall satisfaction using a regression analysis with dummy variables, and discuss the superiority of this methodology with respect to the Importance-Grid approach for the identification of the factor structure of customer satisfaction.