Applying concepts of relational density theory to climate related consumer behavior: A contextual extension study

Abstract

Predicting and influencing consumer behavior can aid in combating the climate crisis. Previously, Matthews et al. (2022) modelled the influence of relational framing on consumer purchasing, where relational training established pro- and anti-environmental coordinated classes. The current paper extends Matthews et al.’s (2022) analysis by empirically modelling complex relational networks consistent with Relational Density Theory (RDT; Belisle & Dixon, 2020). In the experiment, participants completed a pre- and post- relational training multidimensional scaling procedure including positive and negative valence environmental related imagery and unfamiliar symbols. The relational training was designed to establish coordination between the symbols and evaluative climate functions. This analysis allowed for the development of a geometric model of complex relational behavior that were consistent with shifts in purchasing behavior observed in the prior study, supporting the link between relational behavior and overt behavior that may be of interest to behavior and climate scientists. Moreover, the current study provides a direct translational extension of existing research on RDT to a topic of immense social importance.