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 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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".