Exploring gamification in the Administration context: a systematic literature review
Bibliographic record
Abstract
Through a comprehensive review of studies reported in the literature on gamification – which is understood as the use of game design elements in non-game contexts, this work sought to present the results obtained from a Systematic Literature Review (SLR) of studies related to the use of gamification in management and business. This study was conducted based on the application of the Systematic Model for Research in Open Access Databases (SMROAD). The model applied involved the conduct of a comprehensive survey of research studies related to the subject matter investigated in a number of journals and the categorization of the data obtained into two aspects: i) general aspects; and ii) dimension of analysis and its categorizations according to the subject matter. For the literature review, we examined 20 journals, and 24 articles were selected out of a total of 4973 articles initially investigated. The results obtained showed that although research on gamification is incipient and is in need of theoretical and empirical deepening in specific contexts, such as in finance, the use of gamification as a management tool can provide us with relevant individual and organizational results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.030 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".