This paper presents an integrated model of technology acceptance that combines the Technology Acceptance Model (TAM) and the Motivational Model (MM) to better understand how user perceptions are formed before and during the implementation of technology. The model introduces the concept of "user acceptance enablers" (UAEs), which include pre-training interventions and training environment manipulations that influence user perceptions and acceptance of technology. The model is tested using longitudinal data from two studies, and the results indicate that the integrated model is a better predictor of user behavior than the existing models. The integrated model examines the influence of UAEs on intrinsic motivation, perceived ease of use, and perceived usefulness, which are key determinants of user acceptance and continued use of technology. The model also considers the role of behavioral intention in shaping user behavior and the impact of past behavior on future technology use. The results show that intrinsic motivation has a significant positive effect on perceived ease of use and perceived usefulness, and that perceived ease of use has a significant positive effect on perceived usefulness. Behavioral intention is found to be a key determinant of immediate technology use, and past use is a significant predictor of continued use. The integrated model is tested using longitudinal data and is found to be a better fit than the TAM and MM models. The model also shows that UAEs have a significant positive effect on intrinsic motivation and perceived ease of use. The results suggest that training interventions that enhance intrinsic motivation and perceived ease of use can be effective in increasing user acceptance and continued use of technology. The study highlights the importance of considering both pre-training and training environment manipulations in technology acceptance research and suggests that future research should explore the effects of UAEs in different contexts and user populations. The findings have important implications for technology implementation and training, emphasizing the need for early user engagement and the importance of user perceptions in technology acceptance.This paper presents an integrated model of technology acceptance that combines the Technology Acceptance Model (TAM) and the Motivational Model (MM) to better understand how user perceptions are formed before and during the implementation of technology. The model introduces the concept of "user acceptance enablers" (UAEs), which include pre-training interventions and training environment manipulations that influence user perceptions and acceptance of technology. The model is tested using longitudinal data from two studies, and the results indicate that the integrated model is a better predictor of user behavior than the existing models. The integrated model examines the influence of UAEs on intrinsic motivation, perceived ease of use, and perceived usefulness, which are key determinants of user acceptance and continued use of technology. The model also considers the role of behavioral intention in shaping user behavior and the impact of past behavior on future technology use. The results show that intrinsic motivation has a significant positive effect on perceived ease of use and perceived usefulness, and that perceived ease of use has a significant positive effect on perceived usefulness. Behavioral intention is found to be a key determinant of immediate technology use, and past use is a significant predictor of continued use. The integrated model is tested using longitudinal data and is found to be a better fit than the TAM and MM models. The model also shows that UAEs have a significant positive effect on intrinsic motivation and perceived ease of use. The results suggest that training interventions that enhance intrinsic motivation and perceived ease of use can be effective in increasing user acceptance and continued use of technology. The study highlights the importance of considering both pre-training and training environment manipulations in technology acceptance research and suggests that future research should explore the effects of UAEs in different contexts and user populations. The findings have important implications for technology implementation and training, emphasizing the need for early user engagement and the importance of user perceptions in technology acceptance.