Modelling the Effects of User Learning on Forced Innovation Diffusion

TL;DR

This paper investigates how user learning impacts the adoption of forced innovations, specifically smart metering, through agent-based simulations to understand the transition from ignorance to effective use.

Contribution

It introduces an agent-based model to analyze the effects of user learning on forced technology adoption, a previously underexplored area.

Findings

User learning significantly accelerates adoption rates.

Learning reduces resistance to forced innovations.

Simulation results highlight the importance of user education.

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