Expected and Experienced Social Impact of Host Residents During Rugby World Cup 2019: A Panel Data Approach
Bibliographic record
Abstract
Most social impact research considers the pre- and post-event social impacts of sporting events to investigate the effects of these events on residents' or consumers' intention or attitude. This study focused on the qualitative differences between pre-event expected social impacts (T1) and post-event experienced social impacts (T2). Then, it investigated viewing behaviors due to the expected social impacts, and intentions to support events from experienced social impacts. The Rugby World Cup 2019 in Japan provided the context for the study. Panel data were collected from the same Tokyo residents in T1 (3 months before the event) and T2 (4 months after the event). The Internet-based survey consisted of six social impact constructs, framed as expectations in T1 and experiences in T2. Both dependent variables, viewing behavior and supporting events, were measured in T2, after the event occurred. Two expected impacts had a significant positive association with viewing behavior, while three experienced social impacts had a significant positive association with event support intention. The main contribution of this article is extending the understanding of the role of social impact as a predictor variable for residents' behavior and intention to support events by using panel data, which enabled the authors to obtain more robust results. The current study extends the knowledge on consumer expectancy role and social exchange theory in the context of the social impacts of sporting events.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".