Mobile device use at festivals: opportunities for value creation
Bibliographic record
Abstract
Purpose The purpose of this paper is to understand mobile device (MD) use in a festival context. Festivals offer a range of opportunities and activities to use a MD making this context ideal for understanding digital experiences during leisure. The guiding research question asked how do festival attendees use MDs at festivals. The Typology of Human Capability (THC) provided a framework to enhance the understanding of digital experiences at festivals. Design/methodology/approach This research involved six festival case studies where semi-structured interviews were conducted with attendees on-site. Interview questions focused on how festival attendees used MDs during the festival. Data were analyzed using directed content analysis guided by the THC. Findings On-site interviews with 168 attendees revealed that data support the THC dimensions and constructs (sensing, linking, organizing and performing). This typology advances the understanding of the range of digital customer experiences currently available at festivals. Research limitations/implications The addition of context to the THC is recommended to enhance its utility in application. As a limited number of festivals were included, the specific findings may not apply to all festivals but the implications are relevant to a range of festivals. Practical implications Operational definitions of the THC constructs within the festival setting were identified and provide opportunities for developing digital experience offerings. Originality/value This study provided the first comprehensive examination of MD use in festival contexts and in so doing offered data in support of Korn and Pine’s (2011) THC. The findings reveal opportunities for modifying the THC to increase its applicability in a range of settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".