Another Endless November: AOL, WoW, and the Corporatization of a Niche Market
Bibliographic record
Abstract
The entrance of World of Warcraft (WoW) into the massively multiplayer online role-playing game (MMO) market has drastically altered conceptions of how popular a virtual world could be. Currently servicing over 12 million monthly subscribers (Woodcock, 2008), it has vastly exceeded expectations, and has brought with it more new users to persistent virtual worlds than any other product before it. However, while there has been much academic work exploring developments within the game itself (Bainbridge, 2007; Duchenault, et al., 2006; Castronova, 2007), the processes by which this explosive growth has occurred have been under-explored. The growth of World of Warcraft relative to the MMO market can only be explained via its extrinsic characteristics of the game and how these characteristics interact with processes of standardization and diversification with relative to the market as a whole. In this paper, I propose that the process that enabled WoW to rise to its current position as market leader amongst MMOs is remarkably similar to that employed by America Online (AOL) in the early 1990’s, and that the growth of both firms are evidence of the standardizing influence that a globalizing process such as McDonaldization has when it enters a niche market. The parallels that may be drawn between these cases may be instructive in understanding the future growth of MMOs and other virtual environments. I will examine the history of the two firms to find evidence of commonalities between them. I will also outline the parallel corporatist models of McDonaldization and Disneyization as proposed by Ritzer (2000) and Bryman (2004). The process by which these firms grew to dominate their spheres will be examined in this context. I will conclude with an examination of what this growth may mean for the future of the MMO industry.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".