Event portfolios: asset value, risk and returns
Bibliographic record
Abstract
Purpose The research question addressed is whether an event portfolio analysis rooted in financial portfolio theory can yield meaningful insights to complement two approaches to event portfolios. The first approach is extrinsic and rooted in economic impact analysis where events need to demonstrate a financial return on investment. In the second approach events are valued ally, with every event having inherent value and the entire portfolio being valued for its synergistic effects and contribution to social and cultural goals. The paper aims to discuss these issues. Design/methodology/approach Data from visitors to four events in the Sunshine Coast region of Australia are analyzed to illustrate key points, including the notion of “efficient frontier.” Findings Conceptual development includes an examination of extrinsic and intrinsic perspectives on portfolios, ways to define and measure value, returns, risk, and portfolio management strategies. In the conclusions a number of research questions are raised, and it is argued that the two approaches to value event portfolios can be combined. Research limitations/implications Only four events were studied, in one Australian local authority. The sample of residents who responded to a questionnaire was biased in terms of age, education and gender. Social implications Authorities funding events and developing event portfolios for multiple reasons can benefit from more rigorous analysis of the value created. Originality/value This analysis and conceptual development advances the discourse on portfolio theory applied to event management and event tourism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".