Backlash and Sale: A Case Study on Corporate Image and Profitability of Video Game
Bibliographic record
Abstract
Video game industry has rise rapidly. Platforms such as X (formerly known as Twitter), Facebook, TikTok and Reddit has reached millions of users. Many video game developers have implemented social media as a tool of marketing and maintaining PR with its user base. Combining with more integrated marketing campaign and continuous engagement with its user base, corporate image of video game company can lead to consumer loyalty and therefore, effect profitability of the product. This could also backfire in developer’s stead, as consumers are now more aware of their product than ever before. The existence of streaming and video sharing platform such as Twitch and YouTube means consumers can have a clear understanding of the product by watching other people play. Player may also form online community to share the praise or concern of existing product. Many controversial video games produced in recent years have received very different consumer feedback. Some, despite the initial backlash, remain commercially successful, while other faces dire consequences that damage both short-term and long-term profitability. The purpose of this research is to analyze various cases of video game sale when the developer company is under scrutiny by stakeholders due to questionable business or CSR practices. The aim is to understand the relation between a damaged reputation or corporate image and the tangible commercial feedback from the consumers in the context of video game 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".