BIROn - Birkbeck Institutional Research Online

    An applied predictive modeling of brand equity inducing social media brand-related engagement

    Schivinski, Bruno (2018) An applied predictive modeling of brand equity inducing social media brand-related engagement. In: Global Marketing Conference 2018 Global Marketing Conference at Tokyo, 26-29 Jul 2018, Tokyo, Japan.

    [img]
    Preview
    Text
    manuscript_Gamma_1page_SCHIVINSKI.pdf - Author's Accepted Manuscript

    Download (87kB) | Preview

    Abstract

    This study presents a machine learning approach using conditional inference tree (Ctree) to determine how brand equity can be used to factually engage consumers into social media brand-related activities. Using the Ctree algorithm (Hothorn, Hornik, & Zeileis, 2006), a predictive model was computed using self-reported data on consumers’ perceptions of brand equity (Aaker, 1991) and engagement into social media brand-related behavior (Muntinga, Moorman, & Smit, 2011) from a sample of 690 individuals. The predictive modeling analysis revealed 5 different rules (patterns) that trigger social media brand-related behavior. Each rule comprises behavioral engagement discriminating low, medium, and high levels of consumption, contribution, and creation of brand-related social media content. Additionally, the analysis portrait 5 subtypes of consumers according to their behavior. This study has incremental explanatory power over preceding consumer brand engagement studies, in that it demonstrates how to manage brand equity to factually engage consumers into social media brand-related activities, therefore, generating valuable insights that may be used to support business.

    Metadata

    Item Type: Conference or Workshop Item (Paper)
    Keyword(s) / Subject(s): CBBE, COBRAs, social media, Ctree, machine learning, consumer behavior
    School: Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Business School
    Depositing User: Bruno Schivinski
    Date Deposited: 20 Nov 2018 12:06
    Last Modified: 02 Aug 2023 17:44
    URI: https://eprints.bbk.ac.uk/id/eprint/23861

    Statistics

    Activity Overview
    6 month trend
    235Downloads
    6 month trend
    115Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item