POST-EVENT VISITS AS THE SOURCES OF MARKETING STRATEGY SUSTAINABILITY: A CONCEPTUAL MODEL APPROACH
Bibliographic record
Abstract
While extant literature has mainly concentrated on contemporaneous event tourism marketing (i.e., on visiting the city during or around the event) and on intentions to revisit after the event's completion, this research investigates the impact of the event on the decisions of potential tourists/visitors who have never visited the host city and want to visit it after the event's completion. Research in this area, especially in those emerging markets where event marketing is developing rapidly, is limited. In order to address the issues raised, a conceptual model is proposed. This model is based on a multivariate research approach, examining the interrelationships between event image, destination image, participants’ perceived satisfaction with the event and intentions to visit, under the context of non-repeat event marketing. Five hypotheses postulating these interrelationships were tested using structural equation modeling. A “non-repeat” event, the National Games, the biggest traditional sports event in China, was chosen to test this model. Selfadministered questionnaires were used to collect data relating to a period of two months after the event's completion. The findings show that the sustainability of event marketing strategy can be achieved through the post-event visit to the host city.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".