Gamification of Creativity: Exploring the Usefulness of Serious Games for Ideation
Bibliographic record
Abstract
Organizing for idea generation is a recurring challenge in intensive innovation contexts. The literature on ideation has reached a compelling consensus on the features that such organizational devices must possess to support sufficient creativity: learning processes and a creative climate of confidence to promote collaboration. However, current practical methodologies struggle to simultaneously realize these two features. In this paper, we explore the potential ofSeriousGames, a collaborative tool that has been used since the 1960s to facilitate learning processes through the simulation of reality and a role‐playing game, to induce an immersive experience and, more recently, to support the ideation process. To do so, we conducted an exploratory case study using aSeriousGame to support ideation in aFrench medium‐sized business. We then assess the strengths and areas for improvement of thisSeriousGame with respect to an ideation performance framework based on the existing literature. Our findings show thatSeriousGames are efficient tools for supporting existing knowledge exchange between participants and collaboration by providing a creative climate, but they may not sufficiently support learning of the external knowledge required to attain high levels of originality. Accordingly, we discuss some crucial parameters to be further explored to allow for the effective managerial use of such methodologies, such as the fine‐tuning of the knowledge content that serves as a basis for the game.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".