Assessing attendees’ satisfaction at a craft beer festival by means of an importance-performance analysis
Bibliographic record
Abstract
Purpose: Craft beer tourism and beer festivals are subjects of increasing academic discourse, although few theoretical frameworks exist that focus on the measurement of attendee satisfaction at such festivals. Craft beer lovers visit many of these breweries and attend festivals to experience local, regional, national and international brews. The goal of this study is to establish a reliable way of assessing how satisfied attendees were with their experience at a particular craft beer festival. Design/methodology/approach: A quantitative methodology in the form of a survey research design was used. A total of 313 usable questionnaires were collected from attendees at the Capital Craft Beer Festival in Pretoria, South Africa. This study used the importance-performance analysis (IPA) model to determine which event performance attributes would improve customer satisfaction. Findings/results: The findings revealed that 8 items in the importance–performance grid were located in the ‘low priority’ quadrant, 3 items were in the ‘concentrate here’ quadrant, 1 item was in the ‘possible overkill’ quadrant and 15 items were in the ‘keep up the good work’ quadrant, which indicated that those attributes generated high satisfaction and that event organisers should sustain their performance. Practical implications: This study should improve event services for practitioners and policymakers by enabling them to recognise visitors’ demands more easily and to respond better to those demands. Originality/value: This study examined the discrepancies between visitors’ expectations and perceived performance by using the IPA model, which was created by Martilla and James in 1977.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".