Transforming the Fan Experience through Livestreaming: A Conceptual Model
Bibliographic record
Abstract
The purpose of this case study research was to explore how Queensland Rugby League (QRL) used a Facebook brand page, for the Queensland Maroons, and incorporated livestreaming throughout the 2017–2018 State of Origin seasons. Specifically, this study sought to understand managerial perspectives regarding interactive advertising within digital sports marketing strategy encompassing livestreaming and the extent to which it may impact fan engagement. This research utilized a multimethod case study approach involving a content analysis of Facebook, complemented by two semistructured interviews with the organization’s digital staff. The findings revealed livestreaming can be an engaging proposition when it provides exclusive content that allows fans to experience authentic insights into the rituals and traditions of their favorite sports team and athletes in real time. Furthermore, three unique management livestreaming experiences were identified: planning, organization, and delivery (POD). As a response, a conceptual POD model has been created that reevaluates the opportunities for fan engagement adapted from previous research findings (Haimson and Tang 2017 Haimson, O. L., and J. C. Tang (2017), “What makes live events engaging on Facebook Live, Periscope, and Snapchat.” Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pp. 48–60.[Crossref] , [Google Scholar]; Naraine and Karg 2019). This model is important for sports organizations when considering livestreaming, as there may be unique opportunities to focus on interactive advertising and, thus, to develop awareness and the fan-to-fan and fan-to-athlete/sports organization/team relationship.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".