The use of Gamification in Knowledge Management Processes: A Systematic Literature Review
Bibliographic record
Abstract
Purpose: The purpose of this article is to present a systematic literature review that synthesizes the \ninvestigations made into the use of Gamification in Knowledge Management processes in recent years, and a \nconceptual model for analysis of the Gamification of Knowledge Management Systems. \nTheories: Since the last decade the Gamification - defined by the application of game design principles in a non-game \ncontext - as a management practice has become increasingly challenging for researchers. At the height of the \nKnowledge Age, in which we live today, knowledge and the organizational capacity to create, disseminate and retain it is \none of the most important sources of competitive advantage for organizations. As employees’ knowledge is critical for \ncompanies, it is essential to find effective mechanisms to encourage collaborators to share knowledge. In this field, \ngamification is a dynamic to be considered as an enabler of successful knowledge management systems. \nMethodology: A systematic review of the literature was carried out, analyzing the scientific articles obtained through \nelectronic databases, manual research and the cross-referencing of bibliographic references to identify and synthesize \nstudies on the use of gamification in Knowledge Management processes in the period from 2015 to 2018. \nResults: This study demonstrates that the use of gamification in knowledge management processes has a positive \nimpact on employees’ motivation and involvement with these systems, while promoting the creation, transfer and sharing \nof knowledge in the organization. A conceptual model for the gamification of knowledge management systems is \nproposed, intended to be a valid contribution to the operationalization of future studies on the link between gamification \nand knowledge management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".