Mediating social phygital experiences in the context of esports tournaments
Bibliographic record
Abstract
Purpose This study explores consumer experiences shaped by digital technologies, focusing on the evolving concept of “phygital” or hybrid experiences arising from the integration of virtual and physical elements. It specifically investigates the challenges of reconciling performers’ virtual immersion with the physical experiences of crowds at esports tournaments. Design/methodology/approach Using a triadic lens involving producers, performers and audiences, the study employs a comprehensive methodological approach. It combines ethnographic and netnographic data, in-depth interviews and archival data to provide a holistic understanding of the phygital experience. Findings The findings uncover the complexities of value creation and destruction in social phygital experiences. It identifies key strategies employed by producers to mediate these experiences, such as amplifying, bridging, restricting and transposing. These strategies are essential in managing the divergent expectations of performers and audiences, ensuring a cohesive and engaging experience. The findings offer a framework for understanding and managing conflicting expectations in phygital settings. Research limitations/implications Data collection focused primarily on Western esports tournaments. Future research could explore how cultural factors influence the dynamics of social phygital experiences. Practical implications The study provides actionable insights for live performance producers and marketers in esports and other industries, emphasising the need for strategic planning to balance virtual immersion with physical engagement. It highlights opportunities to leverage sensory technologies and atmospheric management to enhance phygital experiences. Originality/value This research advances the conceptualisation of phygital experiences by introducing the notion of social phygitality and demonstrating the importance of triadic management in co-located hybrid events.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".