Influencers, Followers, Anti-Socials and Hackers, oh my! an Analytical Framework of Personas on Social Media Learning Platforms
Research on social media in marketing education has primarily focused on traditional social media platforms, leaving a limited understanding of how purpose-built Social Media Learning (SML) platforms shape learning processes. This gap is notable because novel AI-enabled SML platforms make students’ thinking visible, interactive, and evaluative, altering how they engage with course content and peers. Using an exploratory case study of Perusall, we conduct an abductive qualitative analysis of data from 2022 to 2025 across 31 course-offerings, spanning diverse content and generational cohorts. We conceptualize SML as a distinct educational design layer and exchange-rich learning ecosystem where students co-create knowledge through visible participation. Drawing on Social Exchange Theory (SET), we develop a two-by-two analytic framework, identifying four learner personas (i.e. Influencers, Followers, Hackers, and Anti-socials) based on social engagement and performance orientation. These personas reveal patterned participation logics often obscured in conventional learning environments and highlight how students differentially navigate the rewards and costs of participation. We offer modality-specific teaching recommendations and persona-informed micro-interventions to enhance motivations, social presence, and equitable engagement. This study provides marketing educators with actionable guidance for leveraging SML platforms to design more inclusive, student-centered environments.
Authors
- Rafael Teixeira (ORCID: https://orcid.org/0000-0002-7643-6084)
- Kelley Cours Anderson (ORCID: https://orcid.org/0000-0002-6035-4105)
- Georgianna May (ORCID: https://orcid.org/0009-0005-7807-4821)
Publication Details
- Journal
- Marketing Education Review
- Published
- 2026-10-13
- DOI
- https://doi.org/10.1080/10528008.2026.2733319
- Primary Topic
- Management and Marketing Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00