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Technology adoption theories assume that users' acceptance of an innovativetechnology is on a voluntary basis. However, sometimes users are force toaccept an innovation. In this case users have to learn what it is useful forand how to use it. This learning process will enable users to transit from zeroknowledge about the innovation to making the best use of it. So far the effectsof user learning on technology adoption have received little researchattention. In this paper - for the first time - we investigate the effects ofuser learning on forced innovation adoption by using an agent-based simulationapproach using the case of forced smart metering deployments in the city ofLeeds

Modelling the Effects of User Learning on Forced Innovation Diffusion