Building brand and fan relationships through social media
Bibliographic record
Abstract
Purpose As the popularity of social media increases, sports brands must develop specific strategies to use them to enhance fan loyalty and build brand equity. The purpose of this paper is to explore how two social media platforms were utilised by the Grand Slam tennis events to achieve branding and relationship marketing goals. Design/methodology/approach A content analytic design was employed to examine Twitter and Facebook posts from the official accounts during, and post-, each respective event. Findings Both sites were utilised to cultivate long-term relationships with fans and develop brand loyalty, rather than to undertake short-term marketing activations. However, these sites appear to serve a different purpose, and therefore unique strategies are required to leverage opportunities afforded by each. Interestingly, brand associations were utilised more frequently during the post-event time period. Practical implications This study offers practitioners with useful insight on branding and relationship-building strategies across two social platforms. These results suggest that strategies appear dependent on the event, timeframe and specific platform. Moreover, the events’ differences in post use and focus may also indicate some differences related to event branding in an international context. Furthermore, sport organisations should look to leverage creative strategies to overcome limitations that platform-specific functionality may impose. Originality/value This study offers unique insights brand-building efforts in an international event setting, which differ in a range of contextual factors that impact on social media utilisation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".