Dr. George Dagliyan’s research examines the adoption of AI-enabled technologies through a balanced and structured framework that integrates both facilitating and inhibiting factors. Moving beyond traditional technology acceptance models, his work introduces a comprehensive empirical model that captures how perceived convenience, customization, and efficiency coexist with perceived uncertainty, privacy risk, and loss of control risk in shaping adoption decisions. Rather than treating these forces as opposite ends of a single continuum, his research demonstrates that they operate independently and simultaneously.

His doctoral dissertation introduced a novel framework that accounts for the role of subjective ambivalence arising from the coexistence of positive and negative evaluations of AI-enabled technologies. Using structural equation modeling and large-scale U.S. consumer data, he empirically tested the model across three AI categories—robotic AI (autonomous vehicles), virtual AI (smart home virtual assistants), and embedded AI (telemedicine). His findings reveal that facilitators and inhibitors jointly generate ambivalence, which in turn influences both intention to use and continued usage.

Additionally, his research advances the literature by positioning brand trust—specifically brand reliability and brand benevolence- as a critical antecedent influencing both facilitators and inhibitors. By integrating trust, ambivalence, and dual adoption forces into a unified model, his work provides both theoretical contributions to the diffusion of innovation and practical levers for organizations seeking to accelerate AI adoption.

His research has been presented at the Americas Conference on Information Systems (AMCIS), where it received top paper recognition.