A Review of the Use of Persuasive Technologies to Influence Sustainable Behaviour
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".