Conjoint analysis is often seen as one of the most important methodological developments of marketing research. Various textbooks give an introduction to conjoint analysis and alternative preference measurement methods. Applying variants of conjoint analysis has become less effortful because commercial software is available that makes setting up a conjoint survey and analysing the data very easy. Nevertheless, researchers and practitioners have to make numerous decisions how to design their conjoint studies, such as how to select the attribute and levels when planning their first conjoint study. Decisions on these important ‘craft’ factors will substantially influence the reliability and validity of the preference measurement results and are therefore of key importance to researchers and practitioners who lack sufficient experience using conjoint analysis. Available textbooks rarely discuss these ‘craft’ factors in much detail. Thus, the main goal of this paper is to bring these ‘craft’ factors into focus and sensitize researchers and practitioners towards potential pitfalls that could negatively affect the validity of their preference measurement studies.To achieve this goal, the paper first introduces conjoint analysis and explains its’ key value for research and practice. We then explicate commonly used and more recently developed decompositional, hybrid, and compositional preference measurement approaches and discuss advantages and disadvantages in different decision contexts. Our objective is to guide the potential user in the selection process to an appropriate approach. The second objective of the paper is a discussion of additional important ‘craft’ factors. We deliberate on how researchers can generate and select attributes and levels for their preference measurement studies as well as how they can explain them to respondents. Our third objective is to provide users with more practical guidance how to interpret preference measurement results. Moreover, we aim to enable users to assess the quality of their conjoint data with respect to reliability, validity and applicability measures. We finally also show how market simulations can be set up and how the external validity can be measured. In sum, our paper, thus, serves as a user’s guide to the ‘galaxy’ of consumer preference measurement, and in particular conjoint analysis.
Diese Seite ist durch reCAPTCHA geschützt und es gelten die Google Datenschutzbestimmungen und Nutzungsbedingungen.
