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Simulating user learning in authoritative technology adoption: an agent based model for council-led smart meter deployment planning in the UK

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

Simulating user learning in authoritative technology adoption: an agent based model for council-led smart meter deployment planning in the UK Thumbnail


Authors

Tao Zhang

Uwe Aickelin



Abstract

How do technology users effectively transit from having zero knowledge about a technology to making the best use of it after an authoritative technology adoption? This post-adoption user learning has received little research attention in technology management literature. In this paper we investigate user learning in authoritative technology adoption by developing an agent-based model using the case of council-led smart meter deployment in the UK City of Leeds. Energy consumers gain experience of using smart meters based on the learning curve in behavioural learning. With the agent-based model we carry out experiments to validate the model and test different energy interventions that local authorities can use to facilitate energy consumers' learning and maintain their continuous use of the technology. Our results show that the easier energy consumers become experienced, the more energy-efficient they are and the more energy saving they can achieve; encouraging energy consumers' contacts via various informational means can facilitate their learning; and developing and maintaining their positive attitude toward smart metering can enable them to use the technology continuously. Contributions and energy policy/intervention implications are discussed in this paper.

Citation

Zhang, T., Siebers, P., & Aickelin, U. (2016). Simulating user learning in authoritative technology adoption: an agent based model for council-led smart meter deployment planning in the UK. Technological Forecasting and Social Change, 106, https://doi.org/10.1016/j.techfore.2016.02.009

Journal Article Type Article
Acceptance Date Feb 17, 2016
Online Publication Date Mar 4, 2016
Publication Date May 1, 2016
Deposit Date Jun 21, 2016
Publicly Available Date Mar 29, 2024
Journal Technological Forecasting and Social Change
Print ISSN 0040-1625
Electronic ISSN 0040-1625
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 106
DOI https://doi.org/10.1016/j.techfore.2016.02.009
Keywords Authoritative technology adoption; User learning; Smart metering; Agent-based simulation
Public URL https://nottingham-repository.worktribe.com/output/782939
Publisher URL http://www.sciencedirect.com/science/article/pii/S0040162516000512

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