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

📝 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.

💡 Analysis

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.

📄 Content

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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 Tao Zhang a, Peer-Olaf Siebers b, Uwe Aickelin c

a Department of Marketing, Birmingham Business School, University of Birmingham

b, c Intelligent Modelling & Analysis Group, School of Computer Science, University of Nottingham

Corresponding Author’s Email: t.zhang.1@bham.ac.uk

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.

Keywords:

Authoritative technology adoption, user learning, smart metering, agent-based simulation

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1. Introduction Technology adoption (or Innovation diffusion) theories focus on understanding how, why and at what rate innovative ideas and technologies spread in a social system (Rogers, 1962). In technology adoption processes the decisions of whether to adopt an innovative technology can either be made by the actual users freely and implemented voluntarily, or be made by a few authoritative individuals and implemented enforcedly.
   In the former type of technology adoption, it is usually assumed that before an actual user makes the adoption decision of a particular technology, he/she has learned some knowledge or even gained some experience about it (e.g., the information search stage in the five-step consumer decision model (Engel et al, 1995)).
   In the latter type of technology adoption, once the adoption decision has been made the actual users would be “forced” to use a technology with very limited prior knowledge/experience about it. This type of technology adoption usually takes place at the level of a massive system or infrastructure upgrade. An example for such a case is a university-wide systematic upgrade of the office and lab computer operating system from Windows XP to Windows 7. In this case, the decision is made by the management of the university, and the actual users (e.g. faculty staff and students) are forced to use the innovation with limited or even no knowledge about it beforehand and no influence on the choice1.
   Whilst free adoption decisions and voluntary use in innovation diffusion received intensive studies (e.g., Griliches, 1957; Mansfield, 1961; Rosenberg, 1972; Geroski, 2000; Hall & Khan, 2002), authoritative adoption decisions and forced use in innovation diffusion seems to be an area in its infancy stage. An innovation cannot benefit the society unless its actual users use it effectively. Thus when an authoritative adoption happens, it is significantly important to understand how actual users start to learn about the innovative technology, use the technology, and finally make the best use of

1 We acknowledge that in technology adoption (or innovation diffusion) studies there is a concept “induced diffusion”, which has been defined as “any intervention that aims to alter the speed and/or total level of adoption of an innovation by directly or indirectly internalising positive and/or negative externalities” (Davies & Diaz-Rainey, 2011; p.1229). Induced diffusion research primarily investigates how the diffusion of new technologies can be altered by policy interventions, e.g. economic incentives, information provision or regulations (Diaz-Rainey, 2009). The preponderance of induced diffusion studies use economic modelling approaches based on firm-level data to examine the macro-level patterns of technology diffusion (Diaz-Rainey, 2009). These studies do not look at the adoption decision-making and post-adoption learning behaviour of individual adopters. As noted by Diaz-Rainey (2009, p.20), “there is clearly a need to understand whether inducing

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