Game Marketplace Research Trends: A State of the Art Bibliometric Analysis
Bibliographic record
Abstract
This study examines research patterns in the game marketplace as a crucial factor for achieving success in the gaming industry.This research utilized a bibliometric analysis of 1000 publications obtained from Scopus from 1973 to 2025.The bibliometric examined notable trends and collaborative trends in gaming marketplace research within the business domain.Additionally, supporting analyses like annual contributions, three-field analysis, temporal source production, contributing countries, word clouds, clusters, networks, and theme evaluations are integrated to augment the comprehension of the current status of gaming marketplace research.The bibliometrics indicate that the study trends in the game marketplace for 2024 are linked to supply chain management, blockchain, marketplaces, e-commerce, and commerce platforms.There has been a consistent increase in the game marketplace research since its initial identification.The examined countries, including the USA, China, the UK, India, and Canada, represented the top five nations in scientific production related to gaming marketplace research.Indonesia ranks seventh in terms of scientific production.The most prevalent keywords in the word cloud are game theory, commerce, electronic commerce, sales, competition, and others.This study theme discovered ten prominent cluster networks.The most prominent thematic map identified pertains to automotive themes.This investigation verifies the persistence of research trends and trajectories inside the game marketplace.This approach aids both researchers and the gaming industry identify and leverage pertinent research advantages.University in the Philippines.In 2024, he was acknowledged as one of the top 2% of scientists worldwide.By the end of 2024, she will have made important contributions to 155 indexed Scopus papers, many of which are published in esteemed journals.Michael Nayat Young serves as the Dean of the Industrial Engineering and Engineering Management department at Mapua University in the Philippines.His principal research concentrates on consumer behavior and financial behavior.By the end of 2024, she had significantly contributed to 137 indexed Scopus papers, several of which attained high q-rankings in esteemed journals.
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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 | 0.015 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.175 | 0.221 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".