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AbstractThis paper focuses on multi‐attribute design problems in which several quantitative and/or qualitative attributes are simultaneously involved. Preference elicitation methods dealing with decision‐makers' preferences are often employed to solve such problems. The present paper proposes a conceptual framework that considers preference elicitation as a process shaped by the interaction of different factors including the researcher, methodology, questionnaire and respondent in a given context, at the same time. The aim of the framework is to assist in choosing a decision support tool among various multi‐attribute decision‐making methods, as well as in evaluating and understanding preference elicitation results. To illustrate and exemplify the use of the framework, it is applied to a real‐world case of end user preferences of mobile service platforms. We make use of two methods that are suitable for such a setting: analytic hierarchy process and conjoint analysis. Albeit with considerable discrepancies, the results show that both methods provide insightful information on the principle. Nonetheless, to a large degree, these methods perform differently in the case of non‐complex tasks and unknowledgeable respondents, which the process view aids in evaluating and understanding. Copyright © 2015 John Wiley & Sons, Ltd.

Real option analysis offers interesting insights on the value of assets and on the profitability of investments, which has made real options a growing field of academic research and practical application. Real option valuation is, however, often found to be difficult to understand and to implement due to the quite complex mathematics involved. Recent advances in modeling and analysis methods have made real option valuation easier to understand and to implement. This paper presents a new method (fuzzy pay-off method) for real option valuation using fuzzy numbers that is based on findings from earlier real option valuation methods and from fuzzy real option valuation. The method is intuitive to understand and far less complicated than any previous real option valuation model to date. The paper also presents the use of number of different types of fuzzy numbers with the method and an application of the new method in an industry setting.