Exploring the impact of game framing on the motivational appeal of persuasive strategies and their effectiveness in behaviour change games
Bibliographic record
Abstract
To enhance the persuasiveness of behaviour-change games, designers employ persuasive strategies. These persuasive strategies are intended to motivate the users towards the desired behaviours. Hence, the motivational appeal of these persuasive strategies can play an important role in the effectiveness of these behaviour-change games. Furthermore, research has shown that game framing can impact its effectiveness. Therefore, it is important to understand how the type of framing employed in the game impacts the effectiveness of persuasive strategies and their motivational appeal. To advance research in this direction, this paper explores the relationship between the perceived effectiveness of four popular persuasive strategies (reward, competition, praise and suggestion) and their motivational appeal in a persuasive game for healthy eating across the three different game framings: gain-framing, loss-framing or gain-loss-framing. In a study of 371 participants, our results revealed that all the persuasive strategies were perceived to be significantly effective across all game-framing versions. We also discovered that game framing had varying significant impacts on the relationship between the perceived effectiveness of persuasive strategies and their motivational appeal. We conclude by offering some insights on how to implement persuasive strategies to design games with better persuasive motivational appeal.
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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 | 0.010 | 0.074 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".