Eliciting brand-related social media engagement: A conditional inference tree framework

This study presents a machine learning approach using conditional inference tree (Ctree) to determine cognitive patterns that elicit consumer engagement into social media. Using the Ctree algorithm, a predictive model was computed using self-reported data on consumers' perceptions of brand equity and engagement into brand-related social media behavior from a sample of 1356 individuals. The predictive modeling analysis revealed 5 different cognitive patterns (rules) that stimulate brand-related social media behavior. Each rule comprises behavioral engagement discriminating low, medium, and high levels of consumption, contribution, and creation of brandrelated social media content. Furthermore, based on the different patterns, the analysis portrait a typology of 5 subtypes of consumers according to their behavior, which by complementing the predictive analysis information may be used to stimulate different levels of consumption, contribution, and creation of brand-related social media content.