Information technology (IT) acceptance research has yielded many competing models, each with different sets of acceptance determinants. In this paper, we (1) review user acceptance literature and discuss eight prominent models, (2) empirically compare the eight models and their extensions, (3) formulate a unified model that integrates elements across the eight models, and (4) empirically validate the unified model. The eight models reviewed are the theory of reasoned action, the technology acceptance model, the motivational model, the theory of planned behavior, a model combining the technology acceptance model and the theory of planned behavior, the model of PC utilization, the innovation diffusion theory, and the social cognitive theory. Using data from four organizations over a six-month period with three points of measurement, the eight models explained between 17 percent and 53 percent of the variance in user intentions to use information technology. Next, a unified model, called the Unified Theory of Acceptance and Use of Technology (UTAUT), was formulated, with four core determinants of intention and usage, and up to four moderators of key relationships. UTAUT was then tested using the original data and found to outperform the eight individual models (adjusted R 2 of 69 percent). UTAUT was then confirmed with data from two new organizations with similar

Web-based training is increasingly gaining popularity both in industry and education. Although a number of studies, experiments, and developments have been conducted in this area, few evidence cases of success have been reported. One likely reason for the lack of success is that just placing lecture notes on the web does not train. This situation can be improved through the use of training software such as Intelligent Tutoring Systems (ITS). ITS incorporate built-in expert systems in order to monitor the performance of a learner and to personalize instruction on the basis of adaptation to learners ’ learning style, current knowledge level, and appropriate teaching strategies. However, researchers and developers quickly find out that developing such systems is an enormous task, which is further complicated by the fact that one cannot simply borrow tools from other systems and incorporate them due to various levels of incompatibility at the programming and knowledge base level. To allow for more general ITS, which means that it can be used in other domains, it is required that ITS should be designed and implemented so as to support easy modification of lecture content, modification of decision rules in the expert system, and to support various methods to measure the performances of learning. In this paper, we propose a general framework and data model for web-based adaptive ITS that allows knowledge to be stored in such a way that is not only independent of the knowledge domain, but also supports the storage of transfer knowledge relationships and prerequisite knowledge relationships. We expect that our unified approach could contribute to the establishment of cumulative research traditions in ITS studies.