Are esports spectators fickle? An empirical analysis of esports viewership in the attention economy
Bibliographic record
Abstract
Research question This study focuses on the economics of attention in esports streaming, where spectators are easily distracted. While consumers often divide attention based on interests, there is insufficient exploration into how attention impacts viewership habit formation over time. In this study, we thus seek to understand how esports consumers allocate attention and form habits, providing insights into the economics of attention in digital media.Research methods Panel data (n = 2,048) consisting of monthly hours per esports viewer were collected from Twitch, the top esports streaming site. A standard model of rational habit formation was estimated using Two-Stage Least Squares (2SLS) regressions to control potential endogeneity in past and future viewership.Results and findings Our results indicate past length of viewership is positively related to current length of viewership. However, further analysis reveals future viewership is not significantly related to current viewership, suggesting esports viewing consumption does not follow a rational decision process.Implications Our findings suggest online spectators are exposed to a greater supply of esports content while the demand for their attention becomes scarcer – this advances our understanding of the fleeting nature of viewership behavior in the digital realm. Our findings also provide practical implications for esports content providers and stakeholders seeking to understand the distinct consumer behaviors of esports fans.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".