Design of discrete choice experiments adapted to the respondent's cognitive process.

Discrete choice experiments (DCEs), which involve respondents choosing among alternatives presented in choice sets, are widely used to study preferences for attributes of products or services in various economic fields. To maximize the power of the statistical inference from data from DCEs, it is crucial to design the experiments optimally. Most research in this area focuses on optimizing the design of DCEs under the simplifying assumption that respondents make compensatory decisions. This means that unattractive levels of an attribute can be compensated for by attractive levels of another attribute. However, the assumption of compensatory decision-making often proves to be unrealistic. This research project studies three scenarios in which respondents depart from the compensatory decision rule when making choices: (i) the scenario where respondents ignore attributes in the decision making because there are too many, (ii) the scenario where respondents favor certain attributes because of their position in the description of the alternatives and (iii) the scenario where respondents favor certain alternatives because of their position in the choice set. Pro-actively accounting for respondents' cognitive processes when constructing optimal DCEs in these scenarios will result in more practical designs for DCEs, with applications in marketing, transportation, environmental and health economics.

Keywords:OPTIMAL DESIGN OF CHOICE EXPERIMENTS, CHOICE MODELING, PREFERENCE MEASUREMENT

Disciplines:Applied economics, Economic history, Macroeconomics and monetary economics, Marketing, Microeconomics, Tourism