Nudging Into the Future of Immersive Reality: A Systematic Review of Sustainability‐Orientated Nudges in Policy and the Future Role of Extended Reality ( <scp>XR</scp> )
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Sustainability issues are highly complex and constantly evolving, posing significant challenges for effective management. Addressing these challenges requires innovative policies, such as digital innovations like extended reality (XR) and the application of psychological insights through nudges. This paper examines the intersection of social and behavioral sciences, focusing on how immersive technologies can be used to design and implement nudges for sustainable behavior. We conduct a review and meta‐analysis of nudge effectiveness to better understand sustainability behavior, categorizing nudges as cognitive, affective, or behavior‐oriented. Cognitive nudges are the most studied ( n : 99) but have mixed results (54% positive), while behavioral nudges ( n : 50) are more effective (70% positive). XR studies testing cognitive ( n : 3) and behavioral ( n : 4) nudges generally show positive outcomes, but research on affective nudges is limited (overall n : 15, XR n : 0), likely due to difficulties in testing emotional interventions. XR can enhance cognitive nudges by reducing information processing barriers and expand behavioral nudges by offering convenience not possible in the physical world. Immersive technologies also present new opportunities to test affective nudges by creating virtual scenarios that evoke empathy and social connections. We conclude by emphasizing the need to prioritize ethical considerations in using immersive technologies.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it