From attraction to retention: a qualitative inquiry into AI influencer stickiness
Sudhanshu Bhatt, Anand Jhawar, Sanjeev VarshneyPurpose
The purpose of this paper is to investigate the psychological and behavioral factors that explain why consumers remain engaged with artificial intelligence (AI) influencers. The study specifically explores the construct of “stickiness” – the ability of AI influencers to attract, engage and retain users over time-within the context of digital marketing and strategic brand management.
Design/methodology/approach
A qualitative research design was used, using semi-structured interviews with 37 active AI influencer followers recruited from social media platforms. Thematic analysis, grounded in Gioia principles, was used to identify and interpret the key drivers of user stickiness. NVivo software facilitated systematic coding and theme development.
Findings
The analysis uncovered seven critical themes: AI influencer realism, homophily, information quality, trans-parasocial interaction, interactive stickiness, brand engagement in self-concept and followers’ escapism. Perceived realism and homophily foster trust and emotional connection, while transparent information quality and trans-parasocial interaction are associated with sustained behavioral commitment. The study develops an emergent interpretive framework to explore how cognitive, emotional and behavioral dimensions interact to shape persistent engagement and loyalty in the AI influencer context.
Practical implications
Brands and marketers can leverage AI influencers by prioritizing personalization, authenticity and transparency, enhancing strategic consumer engagement and brand loyalty.
Social implications
The study highlights potential risks associated with excessive AI influencer affiliation, particularly for vulnerable populations such as younger or socially isolated users, emphasizing the need for platform transparency, ethical content design and digital well-being protections.
Originality/value
This paper makes two distinct theoretical contributions. First, it conceptualizes trans-parasocial interaction as a novel AI-specific relational mechanism that transcends classical parasocial frameworks by introducing algorithmically maintained social presence, data-driven reciprocity and engineered homophily that are structurally absent in human influencer contexts. Second, the study re-theorizes six well-established engagement constructs, namely, realism, homophily, information quality, trust, escapism and identity-based brand engagement, for AI influencer settings, demonstrating that algorithmic design and perpetual availability fundamentally alter how these mechanisms are experienced. Together, these contributions extend influencer marketing theory beyond its human-centric foundations, offering academics and practitioners a richer framework for understanding non-human social actors.