Insights from the Review of Apps that Influence Environmental Sustainability
Bibliographic record
Abstract
Environmental sustainability is the avoidance of the depletion of natural resources in order to maintain an ecological balance. To influence people to be environmentally sustainable, several mobile apps exist to teach sustainable behaviours. These apps hold great promise as interventions for influencing people to live sustainable lives since they are often designed using behaviour change strategies. However, the effectiveness of these apps as behaviour change tools is unclear. In addition, despite people downloading these apps, their engagement level is still low. Research suggests that user experience and usability determine the adoption and usage of apps. Research also indicates that user experience and usability of apps can be gleaned from the reviews written by users on the app store. Thus, reviews are a good source of determining the user experience and usability of users. To determine the effectiveness of sustainability apps as behaviour change tools and the users’ experience with the apps, we reviewed 70 sustainability apps that are available on the Google Play Store. First, using a popular behaviour change framework, the App Behaviour Change Scale (ABACUS), we investigated the persuasive strategies implemented by the apps and how these strategies were designed and implemented to achieve the targeted design objectives. Second, using natural language processing, we identified the common themes in the user reviews of the apps. The preliminary results presented here can influence the design of apps for influencing environmentally sustainable behaviour.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".