Comparing consumers’ in-group-favor and out-group-animosity processes within sports sponsorship
Bibliographic record
Abstract
Purpose The paper aims to compare two general team identification processes of consumers’ in-group-favor and out-group-animosity responses to sports sponsorship. Design/methodology/approach The paper draws on two studies and four samples of professional baseball fans in Taiwan ( N = 1,294). In Study 1, data from the fans of three teams were analyzed by using multi-group structural equation modeling to account for team effects and to consider parallel in-group-favor and out-group-animosity processes. In Study 2, the fans of one team were sampled and randomly assigned to assess the sponsors of one of three specific competitor teams to account for differences in team competition and rivalry. In both studies, these two processes were compared using patterns of significant relationships and differences in the indirect identification-attitude-outcome relationships. Findings Positive outcomes of in-group-favor processes were broader in scope and were more pronounced in absolute magnitude than the negative outcomes of out-group-animosity processes across all outcomes and studies. Research limitations/implications The research was conducted in one country and considered the sponsorship of one sport. It is possible that the results could differ for leagues within different countries, more global leagues and different fan bases. Practical implications The results suggest that managers should carefully consider whether the negative out-group-animosity outcomes are actually present, broad enough or strong enough to warrant costly or compromising intervention, because they might not always be present or meaningful. Originality/value The paper demonstrates the comparatively greater breadth and strength of in-group-favor processes when compared directly to out-group-animosity processes.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".