The Impact of Persuasive Framing on the Perceived Effectiveness of a Game for Behaviour Change
Bibliographic record
Abstract
Persuasive games have emerged as effective tools for behaviour change across diverse life domains, engaging and motivating players towards positive behaviours. An essential factor that may influence the effectiveness of persuasive games is their framing, which refers to the strategic design and presentation of the game’s context, mechanics, and objectives. This paper investigates the role of game framing in enhancing the perceived effectiveness of persuasive games or not, using the context of a healthy eating game with three different game framing versions (gain-framing, loss-framing and gain-loss framing). In a study of 371 participants, our results show that while all the framing types were effective at promoting behaviour change, gain-framing was perceived as significantly less effective when compared to loss-framing and gain-loss framing. We also explore potential differences in the perceived effectiveness of four persuasive strategies (reward, competition, praise, and suggestion) implementations across the three framing types. We concluded by exploring the reasons behind these results and their implications for designing persuasive games for various framing types.
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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 | 0.008 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".