Improving means-end-chain studies by using a ranking method to construct hierarchical value maps

Abstract

There is a need for more methodological guidance for researchers using the means-end-chain theory to investigate consumers' motivations for product choice. Particularly, there is a need to improve the quantitative analysis of the qualitative data that constructs the main output of laddering studies: the hierarchical value maps. Existing heuristics do not always successfully address issues associated with: (a) large datasets; (b) variation in response frequencies across levels of abstraction (attributes, consequences, values); (c) differences in response frequencies across groups (e.g. culture); and (d) variation in laddering administration (e.g. “hard” vs “soft” laddering). A new approach is described that uses a “top-down cut-off” strategy, which is driven by the data itself and accommodates the issues described above. The advantages of the method are demonstrated with an example using real data from two cultures ( n=244).