Games for Change—A Comparative Systematic Review of Persuasive Strategies in Games for Behavior Change
Why this work is in the frame
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Bibliographic record
Abstract
Games for change is a growing research field and studies have shown that these games can promote positive behavior change using various persuasive strategies. This article presents a systematic review of 130 persuasive games from the literature published in the last 21 years (2001–2021) to 1) highlight the current trends in the field with respect to domains, year, country, technology platforms, and genre; 2) identify what strategies are employed in the games and their comparative analysis across domains; 3) uncover various ways the persuasive strategies are operationalized in games; 4) explore for possible relationships between persuasive games effectiveness and the number of strategies employed; and 5) highlight gaps and opportunities for future research in the area of persuasive games. Our analysis reveals the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reward</i> strategy is the most popular persuasive strategy employed in the persuasive games’ research. We also uncovered that, even though persuasive games have been strongly effective at promoting behavior change, there was a significant negative relationship between the number of persuasive strategies employed in persuasive games and their overall effectiveness. Based on these findings, we provide insights and design suggestions, operationalization, and assessment for persuasive games.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it