A Review of the Use of Persuasive Technologies to Influence Sustainable Behaviour

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Abstract

Persuasive technologies are interactive systems that are designed to influence people to change their attitudes or behaviours. Persuasive technologies have been used successfully in several domains including health to make people exercise more, shopping to make people buy specific products, and social media to make people contribute better content. In the area of sustainability, its use is not well documented. To contribute to the use of persuasive technologies in sustainability, this paper carries out a literature review of published articles in the area in the past five years and summarizes the main findings based on three main themes: the design and development of the technology to make it adaptive to users, the evaluation of the technology, and the findings from the evaluation. Our results suggest that most persuasive technologies are developed as mobile applications, IoT devices or serious games and the most common behaviour change targeted by the persuasive technologies in this domain are energy conservation and sustainable food management. The most common persuasive strategies that are used are rewards, suggestions and self-monitoring. In terms of evaluation, a self-reported evaluation method was applied by most authors. While the range of evaluation of the developed persuasive technologies was between one hour and one year, the number of recruited participants ranged from two to over nine hundred. The findings from the evaluation were mostly mixed with several authors reporting positive results (behaviour change) for some participants. Based on these results, we suggest considerations for the development of future persuasive technologies for sustainability.

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APA

Adaji, I., & Adisa, M. (2022). A Review of the Use of Persuasive Technologies to Influence Sustainable Behaviour. In UMAP2022 - Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (pp. 317–325). Association for Computing Machinery, Inc. https://doi.org/10.1145/3511047.3537653

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