Exploring the Catalysts and Components of Gamification in Enterprise: A Systematic Literature Review
Bibliographic record
Abstract
This comprehensive review scrutinizes the body of literature on enterprise gamification from 2015 to 2023, drawing from databases such as Elsevier, IEEE, and Google Scholar.A corpus of 37 articles, bearing a close thematic affinity, were examined with an aim to discern the primary impetuses for gamification initiatives and to delineate the most prevalent elements across a broad spectrum of disciplines.It is underscored that gamification emerges as a multidisciplinary concept with far-reaching influence on an array of business operations, spanning marketing, customer engagement, management, health promotion, and software engineering, among others.The analysis discerns six primary categories of drivers prompting the adoption of gamification initiatives: enhancement of customer experience, bolstering employee engagement, process improvement, ensuring security and compliance, fostering organizational awareness and motivation, and promoting well-being.Employee engagement was recurrently cited, with objectives such as cultivating learning, augmenting engagement, and enhancing performance being predominantly highlighted.The findings also reveal an excess of 40 distinct gamification elements, with feedback mechanisms, points, badges, leaderboards, and progress bars being the most frequently employed.The review underlines the necessity of defining clear objectives and identifying pertinent stakeholders prior to the implementation of gamification strategies.Notwithstanding certain limitations, including the exclusion of particular types of publications and the potential oversight of relevant studies, this review furnishes insightful perspectives on the application of gamification in enterprises.Future research is encouraged to delve into the effects of less commonly utilized gamification elements.
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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".