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Conceptual arguments favoring a relational rather than a transactional approach to the study of buyer- seller relationships are now well understood. However, attempts to quantify the factors contributing towards relationship quality have been held back by the complexity of the underlying factors and their interrelatedness. Traditional regression techniques are not effective in analyzing data with high levels of multi- collinearity and missing information, typical in many studies of buyer behavior. The Neural Network technique uses a statistically-based learning procedure modeled on the workings of the human brain which quantifies the relationship between input and output variables through an intermediate “hidden” variable level analogous to the brain. For this study, a neural network was developed with two outcome components of relationship quality:
Reationship satisfaction and trust. And five input antecedents. In a comparison of mutiple regression and neural network techniques, the latter was found to give statistically more significant outcomes. New applications within marketing for neural network analysis are being found.