Affective parameter shaping in user experience prospect evaluation based on hierarchical Bayesian estimation

User Experience (UX) design has become an important factor of product success. One of the important issues involved in UX design is how to evaluate UX. In this research, UX evaluation is quantitatively fulfilled by the cumulative prospect theory, in which UX is perceived from the perspective of the decision making procedure of two alternative design profiles. Furthermore, we study the influence of affective states on UX prospect evaluation through shaping affective parameters involved in UX design. To account for multiple sources of uncertainties, we develop a hierarchical Bayesian model via Markov chain Monte Carlo technique for parameter estimation under three affective states. Also, aircraft cabin interior design is studied as a case study to demonstrate the potential and feasibility of the proposed method.