[Journal First] An Empirical Study of Early Access Games on the Steam Platform
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
is a release strategy for software that allows consumers to purchase an unfinished version of the software. In turn, consumers can influence the software development process by giving developers early feedback. This early access model has become increasingly popular through digital distribution platforms, such as Steam which is the most popular distribution platform for games. The plethora of options offered by Steam to communicate between developers and game players contribute to the popularity of the early access model. The early access model made a name for itself through several successful games, such as the DayZ game. The multiplayer survival-based game reached 400,000 sales during its first week as an early access game. However, the benefits of the early access model have been questioned as well. For instance, the Spacebase DF-9 game abandoned the early access stage unexpectedly, disappointing many players of the game. Shortly after abandoning the early access stage and terminating the development, twelve employees were laid off including the programmer and project lead. In this paper, we conduct an empirical study on 1,182 Early Access Games (EAGs) on the Steam platform to understand the characteristics, advantages and limitations of the early access model. We find that 15% of the games on Steam make use of the early access model, with the most popular EAG having as many as 29 million owners. 88% of the EAGs are classified by their developers as so-called indie games, indicating that most EAGs are developed by individual developers or small studios. We study the interaction between players and developers of EAGs and the Steam platform. We observe that on the one hand, developers update their games more frequently in the early access stage. On the other hand, the percentage of players that review a game during its early access stage is lower than the percentage of players that review the game after it leaves the early access stage. However, the average rating of the reviews is much higher during the early access stage, suggesting that players are more tolerant of imperfections in the early access stage. The positive review rate does not correlate with the length or the game update frequency of the early access stage. In addition, we discuss several learned lessons from the failure of an early access game. The main learned lesson from this failure is that the communication between the game developer and the players of the EAG is crucial. Players enjoy getting involved in the development of an early access game and they get emotionally involved in the decision-making about the game. Based on our findings, we suggest game developers to use the early access model as a method for eliciting early feedback and more positive reviews to attract additional new players. In addition, our findings suggest that developers can determine their release schedule without worrying about the length of the early access stage and the game update frequency during the early access stage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it