Abstract
Graphical presentations of behavioural or semantic networks depicting human phenomena, such as consumer decision-making, have been valued greatly as data distillation tools by both practitioners and academics. However, concerns about the validity of subjective interpretation limit the analytical utility of these traditional approaches, including the analysis of hierarchical value maps (HVMs) derived from means-end chain studies. The authors present an approach for transforming qualitative HVM data into generalised linear models to aid quantitative inferences without compromising the richness of qualitative insights.
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