An investigation of sponsorship effects at charity-linked sporting events: does gender matter?
Bibliographic record
Abstract
The purpose of my dissertation was two-fold. First, this research contributed to an understanding of the effects of the emerging area of cause-related sport sponsorship (CRSS) on consumer perceptions and responsiveness in terms of sponsor interest, favourability, and intended use. Second, this investigation examined the potential influence of gender at all stages of the sponsorship process through a comparison of grouped samples that included respondents of spectators of men‘s versus women‘s hockey, and cancer-cause versus social-cause affiliated events. A proposed framework of consumer processing of CRSS extended earlier findings by Speed and Thompson (2000) and Alay (2008) in highlighting multiple paths of possible influence for both women and men to process sponsorship factors and to respond at the various levels of effect, leading to an investigation of the relationships between five possible predictors of sponsorship response. These included gender, personal involvement (with sport and with cause), gender-support (for women and for men), sponsor-event fit, and perceived sincerity of the sponsor. Field-level data was collected among spectators of five different charity-linked (women‘s and men‘s) hockey events across three different Ontario cities. A total of 314 women and 319 men participated in this study. Findings confirmed the direct and indirect influence of personal involvement, sponsor-event fit, and perceived sincerity of the sponsor on CRSS response. The potential impact of sponsorship at all levels of the hierarchy of effects was also recognized. This study conceptualizes the Diamond of CRSS Goodwill to highlight the expanded platform of consumer engagement offered through these evolved forms of sponsorship. This proposed concept illustrates the interacting effects of goodwill, involvement, and reciprocal return in sponsorships that unite consumers and sponsors with elements of both sport and cause. With regards to gender differences, women expressed significantly greater involvement with social causes than did men. Gender support was also established as a significant and mediating influence on all levels of female consumer response. The answer to whether gender matters in CRSS was discovered to be highly contextual and reflective of complex relationships that are not only based on differences but also on equally important similarities between genders.
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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 | 0.003 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".