PurposeThe purpose of this paper is to analyze user behavior toward multi-screen services by employing neural networks to predict overall customer satisfaction and to prioritize the factors that influence customer intentions.Design/methodology/approachMulti-screen experiences require a new approach incorporating multiple methods. A proposed multi-state analytic approach in which the research model is tested using structural equation modeling was utilized. The results were then used as inputs for a neural network model to predict multi-screen adoption.FindingsThe findings indicate that multi-screen quality significantly influences usability, which subsequently affects the adoption of the technology.Practical implicationsThe policy and managerial implications of multi-screen development are discussed based on the models of acceptance and diffusion.Social implicationsThe emergence of multi-screen services as well as the simultaneous and sequential engagement of users with multiple devices throughout the day challenges the ability of marketers to develop effective communication strategies.Originality/valueThis study provides an in-depth analysis and heuristic data regarding user drivers, market dynamics, and policy implications in the one-source multi-use ecosystem.