Visitors’ Experiences of Cluster Developments at Theme Parks in Malaysia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The spread of social and financial advancement in business environment of developing countries has instigated various expansion approaches, ranging from industry agglomeration to industry clusters, which refers to groups of interrelated firms located in a defined geographical area. Firms or companies that participate in cluster developments benefit from each other in terms of sharing the same territory, infrastructure and services, which has prompted encouraging responses from many industries, including tourism. The Malaysian government licenses intellectual property as well and promotes it to further advance the tourism industry in the country, particularly in the theme park sector. There are 16 theme parks and water parks in the country and 10 more are reported being launched in upcoming years. However, there have also been several closures among theme parks in recent years. Many previous studies have looked into the development of theme parks, focusing on many aspects, including cluster policy and concept, competitiveness, sustainability, safety and security, yet research that looks into cluster development specifically in Malaysia is still scant. The main purpose of this study is to examine whether visitors’ experiences of cluster developments at theme parks in Malaysia have a significant relationship to their intentions to revisit the park. A survey of 312 Malaysian theme park visitors was carried out using a questionnaire, and it concluded that visitors’ experiences of all cluster development dimensions had a significant relationship to their intentions to revisit the park. This finding contributes to an understanding of the importance of visitors’ experiences in theme park tourism and what this means to the tourism industry in the future.
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.
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 | 0.002 | 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 | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it