If you build it, will they log on? Wi–Fi usage and behavior while attending National Basketball Association games
Bibliographic record
Abstract
Purpose Although sports fans have increased their use of digital media to consume sport, especially at professional sport venues, it is unknown the extent to which patrons of said venues are utilizing venue services for these activities. As such, this study asks: (1) How much data do patrons at a sports venue consume via the provided Wi–Fi? and (2) What types of online activity behaviors do Wi–Fi users at sports venues exhibit? Design/methodology/approach This empirical study reports stadia Wi–Fi data usage and consumer behavior from three National Basketball Association venues in the United States: Amway Center in Orlando, FL, Barclays Center in Brooklyn, NY and Target Center in Minneapolis, MN, over a course of 7 games per venue. Findings The findings suggest that Wi–Fi usage is more limited than anticipated. Users who do utilize the venue Wi–Fi do so for very short periods, with the vast majority of user duration lasting between 1 and 10 min. Additionally, the halftime period of games experiences the peak of Wi–Fi usage. Originality/value By increasing our understanding of Wi–Fi usage in venues, this study informs relationship marketing theory research and contributes to the sport management literature. Practically, a better knowledge of Wi–Fi usage is critical, as it constitutes a critical antecedent to develop online marketing strategies.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".