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Grounded in random utility theory, discrete choice experiments (DCE) have proven to be effective in uncovering consumers' choice preferences and switching patterns for repeated choice. Despite this efficacy, a key shortcoming of a DCE is that it does not allow simultaneous comparisons across separate experiments, such as for different product categories, even if both experiments use the same respondents. While wider modelling in a single DCE can use interaction terms as a workaround method to compare across experiments, comparing partworth estimates of separate DCEs is problematic. This study illustrates the use of structural choice modelling (SCM), a recent development that incorporates latent variables and structural equations into the analyses of DCEs and more generally into choice processes. SCM makes it possible to evaluate the consistencies (i.e. heterogeneity) of preferences for attributes common across multiple DCEs when applied to the same respondents, thereby overcoming the stated DCEs' weakness. [ABSTRACT FROM AUTHOR]
