Persuasive Apps for Sustainable Waste Management: A Comparative Systematic Evaluation of Behavior Change Strategies and State-of-the-Art
Bibliographic record
Abstract
With the proliferation of ubiquitous computing and mobile technologies, mobile apps are tailored to support users to perform target behaviors in various domains, including a sustainable future. This article provides a systematic evaluation of mobile apps for sustainable waste management to deconstruct and compare the persuasive strategies employed and their implementations. Specifically, it targeted apps that support various sustainable waste management activities such as personal tracking, recycling, conference management, data collection, food waste management, do-it-yourself (DIY) projects, games, etc. The authors who are persuasive technology researchers retrieved a total of 244 apps from App Store and Google Play, out of which 148 apps were evaluated. Two researchers independently analyzed and coded the apps and a third researcher was involved to resolve any disagreement. They coded the apps based on the persuasive strategies of the persuasive system design framework. Overall, the findings uncover that out of the 148 sustainable waste management apps evaluated, primary task support was the most employed category by 89% (n = 131) apps, followed by system credibility support implemented by 76% (n = 112) apps. The dialogue support was implemented by 71% (n = 105) apps and social support was the least utilized strategy by 34% (n = 51) apps. Specifically, Reduction (n = 97), personalization (n = 90), real-world feel (n = 83), surface credibility (n = 83), reminder (n = 73), and self-monitoring (n = 50) were the most commonly employed persuasive strategies. The findings established that there is a significant association between the number of persuasive strategies employed and the apps’ effectiveness as indicated by user ratings of the apps. How the apps are implemented differs depending on the kind of sustainable waste management activities it was developed for. Based on the findings, this paper offers design implications for personalizing sustainable waste management apps to improve their persuasiveness and effectiveness.
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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 | 0.055 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".