Using Neural Networks to Understand Service Risk in the Holiday Product

Abstract

Despite being one of the most symbolic, important, involving, costly, exciting, uncertain and complicated decisions most consumers make each year, holiday purchasing has received relatively little academic treatment in the service marketing literature. By extending and refining previous work, this study examines the usefulness of perceived risk theory to understand consumers' behaviour in one of the most common holiday experiences, the package holiday. Unlike other studies, this research undertakes a more detailed assessment of the risks involved, as well as examining how consumers reduce those risks. Forty-three risky attributes and fifteen risk reducers were identified and a neural network analysis uncovered a relationship between risk and risk reduction which involved functional, financial and hotel dominated risks, whilst the relationship between risk and purchase intention was mediated by trust in the tour operator and anxiety. The paper explores the nature of the risk in the holiday product and discusses how perceived risk can be measured and used by travel marketers in further research.