Prospect-Theoretic Modeling of Customer Affective-Cognitive Decisions Under Uncertainty for User Experience Design

摘要

In order to incorporate both affective and cognitive factors in the decision-making process, a user experience (UX) evaluation function based on cumulative prospect theory is proposed for three different affective states and two different types of products (affect-rich versus affect-poor). In order to tackle multiple parameters involved in the UX evaluation function, a hierarchical Bayesian model is proposed with a technique called “Markov chain Monte Carlo.” It estimates parameters that represent different cognitive tendencies and affective influences for customers at the individual and group levels by generating posterior probability density functions of the parameters to incorporate inherent uncertainty. An experiment with four hypotheses was designed to test the proposed model. We found that: 1) anxious participants tend to be more risk-averse than those in joy and excitement; 2) joyful and excited participants tend to be more risk-seeking than those in anxiety in UX-related choice decision making; 3) all participants tend to be averse to unpleasant UX; and 4) participants tend to value by feeling for affect-rich products and value by calculation for affect-poor products. Furthermore, the models of five different types can predict choice decision making between product profiles with around 80% accuracy. In summary, the results explain affective-cognitive decision-making behavior in the complex domain of UX design and, thus, illustrate the potential and feasibility of the proposed method.