Quantifying Emotional Flow: Testing the Emotional Flow Hypothesis from a Longitudinal Latent Growth Curve (LGC) Modeling Approach

Abstract

ABSTRACT This paper presents a longitudinal, latent growth curve (LGC) modeling approach to refine the emotional flow measure and hypothesis testing. Emotional flow is operationalized as the marked within-individuals variations in one or more discrete emotions over time, which can be modeled as the amount and shape of change in emotions during message exposure. Emotional flow effects are tested in the LGC framework using data collected from a web-based experimental study where individuals (US Qualtrics Panel, N = 620) read an anti-sugary sweetened beverage message in the standard threat appeal format. Simultaneous fear and hope flows were established with unconditional LGC modeling. The two flows and their interaction were then used to predict message effects outcomes. Results showed that flow effects were nonsignificant when either the fear flow or the hope flow was relatively flat in form, but robust when both emotional flows were with marked variations over the course of message exposure.