Understanding generative AI continuance intention: a dual-factor perspective on facilitators and barriers

Abstract

Purpose This study aims to identify and explain the key factors that encourage or discourage users from continuing to use Generative Artificial Intelligence (AI) platforms. Specifically, the research examines how system-related facilitators (such as interaction quality, personalization, reliability and affordances) and psychological barriers (including inertia, perceived threat and regret avoidance) shape users’ post-adoption attitudes and continuance intentions toward Generative AI.

Design/methodology/approach The survey data were collected from the respondents applying the purposive sampling technique. Partial Least Squares structural equation modeling was used in the analysis.

Findings The study’s results reveal that perceptions of interaction quality, personalization, reliability, creative affordance and analysis affordance significantly promote the intention to continue using. Conversely, perceptions of inertia, threats and regret avoidance significantly hinder continued use.

Research limitations/implications The findings might not be widely generalizable. The data were collected only in a particular community.

Practical implications These insights offer critical implications for business owners, platform developers and policymakers aiming to retain consumers of Generative AI products.

Originality/value To attain the objective, this research integrated the “Elaboration Likelihood Model” and “Status Quo Bias theory”. It developed a conceptual model to address cognitive, emotional and behavioral components.