Academic journal article Academy of Marketing Studies Journal

Modeling Satisfied and Dissatisfied Customers in a B2B Setting

Academic journal article Academy of Marketing Studies Journal

Modeling Satisfied and Dissatisfied Customers in a B2B Setting

Article excerpt


In this paper we propose a simulated approach to model empirical customer satisfaction data in a B2B setting for medium technology offerings. The model is compared to empirical customer satisfaction response data to examine fit. Our model suggests that customer satisfaction response in this setting should be characterized as separate distributions for satisfied and dissatisfied customers. Research and managerial implications are discussed.


Establishing long-term business relationships, a critical component of effective supply chain management, requires understanding customer satisfaction in a meaningful way. While the B2C satisfaction construct is well-studied in the literature, B2B customer satisfaction has received less attention (Patterson, Johnson, & Spreng, 1997; Szymanski & Henard, 2001). Increasing customer satisfaction is expected to increase repurchase intentions, a likely precondition for long-term business relationships. This paper builds on this stream by exploring the implication of modeling separate response distributions for satisfied and dissatisfied customers in medium technology industries in a B2B setting. We develop a series of simulations based on survey data that will allow researchers to explore sensitivity of parameter estimates.

The characterization of customer satisfaction as separate distributions has important implications for the study of customer satisfaction and for marketing practitioners. If customer satisfaction is a single construct then the goal of maximizing average customer satisfaction levels is appropriate. Conversely, if customer satisfaction is a two-factor (or multifactor) construct as theorized here, implications for theory and practice are profoundly different: dissatisfied customers imply separate managerial actions than do satisfied customers and should be modeled separtely. Dissatisfied customers are more visible to researchers and managers when the response distributions are disaggregated as we suggest in this paper.

The paper is organized as follows. First we review the relevant literature on customer satisfaction. Next we develop a series of simulation models to best characterize demand distribution in an empirical sample including theoretical discussion. In the concluding section we discuss research and managerial implications of our work, limitations, and suggested future research.


Importance of Customer Satisfaction

The nature and composition of customer satisfaction are important research topics. Increased customer satisfaction is believed to increase repurchase intentions and enhance longterm financial performance (Mittal, Anderson, Sayrak, & Tadikamalla, 2005). Satisfying customers increases repurchase intentions and loyalty (Kellar & Preis, 201 1). On the other hand, dissatisfied customers are more likely to defect from business relationships and potentially sour relations with additional customers and potential customers through negative word-of-mouth (East, Romaniuk, & Lomax, 2011). Dissatisfied customers may in fact be more likely to disseminate their evaluations than are satisfied customers (citation). Therefore satisfying customers tends to be an emphasized aspect of business strategy, and customer satisfaction measures often play a central role in organizational balanced scorecard systems of measurement (Kaplan & Norton, 2007).

Long-term business relationships increase performance through several mechanisms such as value creation, cost minimization, and customer acquisition. Long-term business relationships allow firms to combine capabilities in unique value-creating ways that neither firm could accomplish independently (Ghosh & John, 1999). An example of this is a small appliance retailer partnering with a large manufacturer to develop training and service procedures for the manufacturer's products. The retailer doesn't have the technical skills to develop the training program on its own and is therefore dependent on the manufacturer for support. …

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