This study extends the understanding of the process of teachers' technology adoption by investigating the dynamic nature of the adoption process. We propose a nonhomogeneous hidden Markov model that reveals the dynamics of teachers' adoption over time and examines the impact of internal and external factors, including experiences, interventions, and heterogeneity of teachers' intention and usage. The model builds its estimates on longitudinal action data from an e-textbook platform with extracted covariates based on direct observations and in-depth interviews. Three latent states representing the adoption dynamics in the data are identified. Results show that teachers encounter difficulty moving to and continuously staying in an active state of technology adoption without exogenous impacts, such as learning from peers and practice in the classroom. In addition to impacts from one's own experiences, inactive teachers benefit from external interventions, whereas teachers in active states benefit from peer demonstrations and experience sharing. The proposed dynamic model allows researchers to distinguish short- and long-term effects that may improve the assessment of interventions. The new approach and findings have implications in dynamically facilitating and sustaining teachers' technology-adoption processes.