Towards a comprehensive methodology for applying enterprise gamification
Bibliographic record
Abstract
Gamification as a new concept uses game elements in a novel way to engage users of a non-gaming system and can be used in many domains within an enterprise, to implement the organizational processes with lower costs, higher quality or in a more efficient way. Although there are many researches on gamification but a few studies can be found in the organizational gamification and there are few research works about framework and methodology for designing and implementing organizational gamification in the literature. The purpose of this article is to provide a comprehensive methodology for the enterprise gamification. This research is an attempt to overcome the mentioned gap via presenting a methodology by applying some important issues including organizational, humanity and gamification aspects together to design and implement customized enterprise gamification solutions through reviewing the related literature and experts’ commentaries. The evaluation of the methodology showed that it is an appropriate and perfect way to design gamification solutions in an organization, besides the enterprise needs to provide the necessary conditions for its implementation. This paper forwards an important debate on a comprehensive methodology for applying enterprise gamification, which explains how to properly use gamification in enterprises to increase productivity and better communication with employees, and thus contributes to literature on internal and enterprise gamification.
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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 | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".