Industry strategy: Post-COVID, Twitch Rivals and videogame companies
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
Purpose Twitch in recent years due to COVID-19 has become a very relevant streaming platform. Among the contents that are streamed on the platform are the events known as Twitch Rivals, which are organized by Twitch itself. These events bring together several of the platform’s biggest streamers to compete in certain video games. The objective of this paper is to analyze the impact of these events on the stock returns of video game companies through the event study methodology and determine possible strategies that lead to positive returns using fuzzy-set qualitative comparative analysis (fsQCA). Design/methodology/approach Event study methodology was applied from 2019 to 2022 with the aim of knowing if the effect is the same or different, since a drop in Twitch statistics has recently been detected, either due to the “return to normality” from COVID-19 and/or to the appearance of new platforms like Kick (Patterson, 2023; Campbell, 2022). Also, the paper analyzes the best strategies that videogame companies could follow on Twitch Rivals to obtain positive returns. For that, fsQCA method was applied. Findings The results obtained suggest that there is indeed an influence of events on stock returns and that this influence is different depending on the year. Moreover, four possible successful strategies were found. Originality/value This paper shows the relationship between Twitch Rivals and the returns of video game companies, showing the relevance that streaming has for them. The paper proposes possible strategies to be considered by video game companies that organize Twitch Rivals to obtain positive returns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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