Enablers and Inhibitors of AI-Powered Voice Assistants: A Dual-Factor Approach by Integrating the Status Quo Bias and Technology Acceptance Model

Abstract This study investigates the factors that build resistance and attitude towards AI voice assistants (AIVA). A theoretical model is proposed using dual-factor framework by integrating status quo bias (sunk cost, regret avoidance, inertia, perceived value, switching costs, threat) Technology Acceptance Model (TAM; ease of use usefulness) variables. The relationship between adoption AIVA, TAM attitudes AIVA. sample four hundred twenty was analysed structural equation modeling to investigate hypotheses. results indicate an insignificant inertia Perceived value found have a negative but significant with Further, also significantly differs across gender (male/female) age groupings. study's are posited as adding extant literature practice, directly related theory, TAM.