The influence of categorical identity on users’ preference for generative AI (GenAI) based applications
(A2026-134063)

AUTHORS

Hanaa Hamidi, University of Bergamo; Giuseppe Pedeliento, University of Bergamo; Julien Cloarec, iaelyon School of Management, Université Jean Moulin Lyon 3, Magellan; Daniela Andreini, University of Bergamo

ABSTRACT

Generative AI (GenAI) based applications are rapidly transforming digital markets, yet their categorical boundaries are still unsettled. As developers navigate this evolving landscape, questions emerge about how they should self-categorize their apps and how such choices shape users’ responses. This study seeks providing answer to the following research question: In the evolving market for GenAI-based applications, should developers anchor their products in a single categorical identity—typically associated with specialist applications—or is it more advantageous to position them across multiple categorical identities, as generalist applications do? Across two quantitative studies, we find that categorical diversity is often associated with lower user evaluations, whereas a focused categorical identity is linked to more favorable responses. At the same time, multi-category applications attract higher downloads, suggesting that broad appeal and GenAI-related hype foster initial adoption. These findings offer actionable insights for developers seeking to understand the strategic and market implications of categorical positioning in emerging technology domains.