Games for Change—A Comparative Systematic Review of Persuasive Strategies in Games for Behavior Change
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 therewardstrategy 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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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 | 0.020 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.029 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".