Balancing value and risk within a city's event portfolio: an explorative study of DMO professionals' assessments
Bibliographic record
Abstract
Purpose This study aims to advance both theory and praxis for event portfolio management in cities and destinations. An experiment has been conducted with professional event practitioners in a city to determine their opinions and strategies for balancing value and risk within their event portfolio. The first objective is to rank 14 of the city's recurring events in terms of both value and risk. Second, the events are plotted in a two-dimensional chart of value versus risk with the objective to differentiate between the 14 events. The third objective is to describe the event characteristics that event professionals associate with value and risk. Design/methodology/approach Results derive from an experiment involving the forced Q-sort procedure and professional event managers from a city renowned as an “event capital”. Empirical evidence is analysed by the constant comparative method of how events are being evaluated by ten professionals working for a DMO. Findings Economic impact and image effects are characteristics of high-value events as is an opportunity to create relations with event owners for future collaboration. Local community involvement is important for all events. The issue of portfolio fit was a common argument for weak-value events. Research limitations/implications Results are based on the opinions of ten DMO employees in one large city. Conclusions help build event portfolio theory. Practical implications The results and methods are useful for event strategists and evaluators. In particular, the management of event portfolios and policies covering events in cities and destinations can benefit from the documented method for explicitly balancing risks with perceived value. Social implications A portfolio perspective is also suggested as an approach to analyse the total tourist attractions portfolio of a destination. Originality/value Opinions regarding public value and risk by civil servants who work with events have not been studied before. The constant comparative method produces results that can be applied to policies governing events. In terms of theory development, concepts from financial portfolio management, product portfolio management and risk management are used to develop event portfolio design and management, and insights are gained on trade-offs in the process. The plot of the events in a two-dimensional chart of value versus risk clearly differentiated the 14 events and is an original contribution.
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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 | 0.000 |
| 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 | 0.000 | 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".