The Impact of Sponsorship on Social Media Engagement: A Longitudinal Examination of Professional Sport Teams
Bibliographic record
Abstract
Social media has become an important frontier in the sport sponsorship paradigm ( Dees, 2011 ), offering brands a powerful mechanism to stimulate consumer engagement ( Vale & Fernandes, 2018 ). Despite this potential, the extent to which social media content, as part of a sport sponsorship's leveraging activities, can yield consumer engagement behaviors is unknown. Thus, the purpose of this study was to examine the impact of integrating sponsors into the social media posts of sport organizations on fan engagement. A total of 13,542 Instagram posts from four professional sports teams were extracted from 2017-2019. A regression analysis revealed that sponsored content negatively affected engagement levels. Consequently, brands need to be more cognizant that simply sponsoring content in an inauthentic, forceable manner may not yield the results they are seeking through their association. Furthermore, sport organizations need to reconsider their social media strategy, working with partners to organically embed sponsors into content.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".