Latent variables as a proxy for inherent preferences: A test of antecedent volition

Magor, Thomas; Coote, Leonard V.

doi:10.1016/j.jocm.2015.01.002

Cited by 1 publication

(2 citation statements)

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“…SCM also allows the simultaneous modeling of multiple choice experiments completed by the same individuals (Bowe et al., ; Magor and Coote, ; Rungie et al., , ). We will use this possibility to analyze discrete choice data for three products and determine whether the preferences for attributes, and the related factors, are constant across products.…”

Section: Factor Modelsmentioning

confidence: 99%

“…As Lusk and Briggeman () pointed out, the consideration of meta‐attributes is certainly appropriate for the analysis of food choices, as consumers can be expected to have more stable preferences for these meta‐attributes compared to single attributes in single choice situations. Additionally, the SCM approach has the advantage that multiple choice experiments can be analyzed simultaneously (Bowe et al., ; Magor and Coote, ; Rungie et al., ). The present study will exploit this possibility by analyzing choice data for pork, eggs, and pasta containing eggs simultaneously and, by doing so, examining whether preferences for the selected credence attributes are consistent over products.…”

Section: Introductionmentioning

confidence: 99%

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Value of labeling credence attributes—common structures and individual preferences

Dahlhausen

Rungie

Roosen

2018

Agricultural Economics

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This article analyzes the common preference structure underlying purchase intentions for food products labeled with credence attributes. In three consecutive, hypothetical discrete choice experiments, consumers selected pork, eggs, and pasta containing egg based on the attributes organic, local, animal welfare, certified "free of antibiotics," and price. The data were analyzed using structural choice modeling, a factor-analytic approach modeling latent sources of preference heterogeneity. The results of this analysis show that preference heterogeneity is stable across products and can be explained best by common characteristics in credence attributes. The article gives guidance as to how to use factors in the analysis of multiple discrete choice experiments.JEL classifications: A13, C35, D12, Q13

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Section: Factor Modelsmentioning

confidence: 99%