Modelling the effects of user learning on forced innovation diffusion
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 fro...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2012
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| Online Access: | https://eprints.nottingham.ac.uk/2068/ |
| _version_ | 1848790714677198848 |
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| author | Zhang, Tao Siebers, Peer-Olaf Aickelin, Uwe |
| author_facet | Zhang, Tao Siebers, Peer-Olaf Aickelin, Uwe |
| author_sort | Zhang, Tao |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T18:17:00Z |
| format | Conference or Workshop Item |
| id | nottingham-2068 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:17:00Z |
| publishDate | 2012 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-20682020-05-04T20:22:58Z https://eprints.nottingham.ac.uk/2068/ Modelling the effects of user learning on forced innovation diffusion Zhang, Tao Siebers, Peer-Olaf Aickelin, Uwe 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. 2012 Conference or Workshop Item PeerReviewed Zhang, Tao, Siebers, Peer-Olaf and Aickelin, Uwe (2012) Modelling the effects of user learning on forced innovation diffusion. In: ORS SW12 Simulation Conference, 27-28 Mar 2012, Worcestershire, England. http://www.theorsociety.com/Pages/ImagesAndDocuments/documents/Conferences/SW12/Papers/ZhangSiebersAickelin.pdf |
| spellingShingle | Zhang, Tao Siebers, Peer-Olaf Aickelin, Uwe Modelling the effects of user learning on forced innovation diffusion |
| title | Modelling the effects of user learning on forced innovation diffusion |
| title_full | Modelling the effects of user learning on forced innovation diffusion |
| title_fullStr | Modelling the effects of user learning on forced innovation diffusion |
| title_full_unstemmed | Modelling the effects of user learning on forced innovation diffusion |
| title_short | Modelling the effects of user learning on forced innovation diffusion |
| title_sort | modelling the effects of user learning on forced innovation diffusion |
| url | https://eprints.nottingham.ac.uk/2068/ https://eprints.nottingham.ac.uk/2068/ |