Modelling the effects of user learning on forced innovation diffusion

Zhang, Tao; Siebers, Peer-Olaf; Aickelin, Uwe

Abstract

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.

Citation

Zhang, T., Siebers, P., & Aickelin, U. Modelling the effects of user learning on forced innovation diffusion.

• Conference Name: ORS SW12 Simulation Conference
• End Date: Mar 28, 2012
• Deposit Date: Jul 19, 2013
• Peer Reviewed: Peer Reviewed
• Public URL: https://nottingham-repository.worktribe.com/output/1009489
• Publisher URL: http://www.theorsociety.com/Pages/ImagesAndDocuments/documents/Conferences/SW12/Papers/ZhangSiebersAickelin.pdf