Investigating the Impacts of Customer Experience and Attribute Performances on Overall Ratings using Online Review Data: Nonlinear Estimation and Visualization with a Neural Network

This study investigates interpretable neural networks for marketing and consumer behavior research using customer reviews instead of measurement scales to better understand customer experiences. Service attribute ratings are used to measure attribute performances to compare the influence of customer experience and service performance on overall satisfaction. Although many researchers have investigated word-of-mouth reviews and their practical applications, the detailed contents of those reviews were generally disregarded, possibly because of their high dimensionality. To solve this problem, this study proposes some useful neural-network methods for specifying the expected assumptions based on previous knowledge or theories in consumer behavior research. Because neural networks help estimate nonlinear relationships between objective and predictive variables, a partial dependence plot is used to visualize the estimated functions and marginal effects. Empirical results not only provide a highly accurate neural-network model, they also create better marketing implications.