Daily Quests or Daily Pests? The Benefits and Pitfalls of Engagement Rewards in Games
Bibliographic record
Abstract
Many games use engagement rewards as incentives for players to engage, e.g., daily login rewards, repeatable challenges, or seasonal rewards like holiday skins. These rewards may serve players by facilitating enjoyment or motivation; however, they may also be considered differently by skeptical players, e.g., as dark patterns that do not benefit players, and may detract from-or even harm-player experiences. As they are widely prevalent in a variety of games, it is important to understand how such rewards are experienced by players to inform potential pitfalls, such as when they are negative for gaming experience or lead to unhealthy gaming behaviours. 178 participants completed a mixed-methods survey and described such rewards in games they play, the tasks required to acquire them, and their experience qualitatively and with validated scales of motivation regulation and passion orientation. We found that players perceived these rewards as beneficial (e.g., as motivation), as negative (e.g., by promoting fear of missing out), or even as an obligation or chore. Quantitative results further support the dualistic experience of such rewards. We contribute findings and design recommendations that are useful for understanding and designing widely used but potentially detrimental reward mechanics.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".