This dissertation aims at developing and testing a new method that can better capture preferences for multistate health profiles. The motivation arose from the failure of the QALY (Quality-Adjusted Life Year) model in adequately capturing preferences in multistate health profiles. The current QALY-based technique captures preferences for multistate health profiles by evaluating each health state in the profile independently of other states. As the past literature showed, this additive independence condition does not hold in practice and hence such approach is inadequate. To address this issue, this study proposes a novel approach to measure preferences for multistate health profiles by looking at two consecutive health states at a time. It hypothesizes that an evaluation of the future health state is dependent or "conditioned" on the level of the preceding, or current, health state. Characteristics of the current health state that are suspected to impact the resulting conditional preference scores for future health state are systematically explored in a carefully designed empirical study. The interested factors include duration of the current health state, direction of change and amplitude of change between the current and future health states. A 2

Consumer preference measurement is a quantitative field of study for modeling, collecting and analyzing product decisions by consumers. Discovering how consumers choose products is an important area of marketing research and recognized as a successful partnership between academic theory and practice over the past forty years. Despite preference measurement’s success in consumer products, little guidance is available for its application to software product management. This paper assesses the feasibility of applying advanced preference measurement techniques to software products and suggests a framework for conducting such studies. A summary of the methods is provided to give guidance to software product managers seeking to apply preference measurement to common product decisions. The paper concludes by recommending a technique called ‘maximum difference scaling’ to elicit customer feedback to help measure the importance of new features for software product improvement. / text