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Consumption ritual has been used to understand the meanings of consumption and consumer behavior, however less attention has been focused on the role of ritual in connoisseurship consumption and how consumption rituals can transform the consumer’s tastes. What is the role played by consumption ritual in connoisseurship taste?

Drawing on key concepts from ritual and taste theories and a qualitative analysis of the North American specialty coffee context, the authors address this question introducing the idea of connoisseurship taste ritual which is based on novelty coffee consumption practices that are opposite of the traditional or regular practices. The data collection set in the United States and Canada includes 15 consumer in-depth interviews, participant observation in 36 independent coffee shops in Canada and the United States, a Specialty Coffee Association of America event, and three barista coffee competitions. The body of qualitative data was interpreted using a hermeneutic approach.

The authors introduce the connoisseurship taste ritual which has several dimensions: (1) variation in the choices of high-quality products, (2) the place to perform the tasting, (3) the moment of tasting, (4) the tasting act, (5) perseverance, and (6) time and money investment.

This research paper extends the notion of consumption ritual introducing the connoisseurship taste ritual and also extends the theories of taste by explaining how, regarding a specific aesthetic category of product, people develop different tastes through ritualistic consumption.

In this paper we show, by means of an example of its application to the problem of house price forecasting, an approach to attribute selection and dependence modelling utilising the Gamma Test (GT), a non-linear analysis algorithm that is described. The GT is employed in a two-stage process: first the GT drives a Genetic Algorithm (GA) to select a useful subset of features from a large dataset that we develop from eight economic statistical series of historical measures that may impact upon house price movement. Next we generate a predictive model utilising an Artificial Neural Network (ANN) trained to the Mean Squared Error (MSE) estimated by the GT, which accurately forecasts changes in the House Price Index (HPI). We present a background to the problem domain and demonstrate, based on results of this methodology, that the GT was of great utility in facilitating a GA based approach to extracting a sound predictive model from a large number of inputs in a data-point sparse real-world application.