Service withdrawal: The uncertain future of the games-as-a-service model
Bibliographic record
Abstract
The last decade has seen a rapid growth of service-based offerings – also known as Game-as-a-Service or GaaS – in the video game industry, among which are some of the most popular franchises, such as Fortnite or League of Legends . Yet, even though these games are designed to be played and supported for an indefinite period of time, many studios have recently chosen to curtail services after introducing them, making for unclear outlooks on the future of this business model. Hence, based on secondary sources – including company documents, industry press and dedicated player forums – this multiple case study sets out to investigate the reasons behind studios’ decision to discontinue parts of five popular GaaS. Three main motives for withdrawing services emerge from the cases. Namely, rather than supporting services, studios decide to (1) attend to the company, to (2) attend to players and (3) to attend to the core product itself. The results contribute to the nascent GaaS literature, in particular with respect to business models and product-life cycle considerations. Implications for studios are offered in closing.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".