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 investigations made into the use of Gamification in Knowledge Management processes in recent years, and a conceptual model for analysis of the Gamification of Knowledge Management Systems. Theories: Since the last decade the Gamification - defined by the application of game design principles in a non-game context - as a management practice has become increasingly challenging for researchers. At the height of the Knowledge Age, in which we live today, knowledge and the organizational capacity to create, disseminate and retain it is one of the most important sources of competitive advantage for organizations. As employees’ knowledge is critical for companies, it is essential to find effective mechanisms to encourage collaborators to share knowledge. In this field, gamification is a dynamic to be considered as an enabler of successful knowledge management systems. Methodology: A systematic review of the literature was carried out, analyzing the scientific articles obtained through electronic databases, manual research and the cross-referencing of bibliographic references to identify and synthesize studies on the use of gamification in Knowledge Management processes in the period from 2015 to 2018. Results: This study demonstrates that the use of gamification in knowledge management processes has a positive impact on employees’ motivation and involvement with these systems, while promoting the creation, transfer and sharing of knowledge in the organization. A conceptual model for the gamification of knowledge management systems is proposed, intended to be a valid contribution to the operationalization of future studies on the link between gamification and knowledge management.
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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 | 0.031 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".