  Technology adoption theories assume that users' acceptance of an innovative
technology is on a voluntary basis. However, sometimes users are force to
accept an innovation. In this case users have to learn what it is useful for
and how to use it. This learning process will enable users to transit from zero
knowledge about the innovation to making the best use of it. So far the effects
of user learning on technology adoption have received little research
attention. In this paper - for the first time - we investigate the effects of
user learning on forced innovation adoption by using an agent-based simulation
approach using the case of forced smart metering deployments in the city of
Leeds
