Service-dominant logic and the festival experience
Bibliographic record
Abstract
Purpose The purpose of this paper is to understand the process of value co-creation by examining festival attendees’ perspectives of their festival experiences. Service-dominant logic (SDL) is used as a framework to understand the how value is co-created in the festival setting. Design/methodology/approach Using a SDL approach and personal meaning mapping methods, this research offers insight into how value is co-created by the attendee, festival, and influential others. Findings This research found that personal, social, cultural, physical, place, and arts presentation domains come together to add value to the festival experience. Research limitations/implications This research adds insight into the value co-creation process if festival settings. SDL is examined in relation to findings and re-conceptualized based on findings. This research was not intended to generalize all performing arts festivals but instead provided a detailed descriptive account of the experiences offered by performing arts festivals examined. Practical implications These findings contribute to the understanding of how co-created experiences can be developed, marketed and managed and provide insight into areas of future research to better understand the co-creation process in event contexts. Originality/value By providing a framework for understanding the festival experience, employing SDL, and using of experiential assessment methods across festivals, this research fulfils an identified need for an in-depth understanding of the co-created meanings of festival experiences.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| 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".