The role of project management, public open space management and environmental quality on visitor comfort in tourism area
Bibliographic record
Abstract
The purpose of this study was to analyze the relationship between public open space management and visitor comfort in tourist areas and environmental quality. The approach used in this study is a quantitative approach. The type of research used in this study is non-experimental, used to find relationships between variables. Based on its objectives, this study is a correlational study that seeks relationships or associations between two or more variables used in the study. The respondents of this study were 254 visitors to tourist attractions selected using a simple random sampling technique based on the ease of finding potential participants from trusted sources (participants who have completed the questionnaire). The researchers also contacted potential respondents who matched the characteristics of the participants through direct messages on WhatsApp, Line, and Instagram. Next, questionnaires were given to a number of respondents through social media such as email, WhatsApp, Line, and direct messages on Instagram. This questionnaire uses an ordinal scale using a Likert scale. The scale used in data collection in this study is a scale with a range of 1 to 5. The analysis technique used in this study is Partial Least Squares-Structural Equation Modeling (PLS-SEM) which was carried out using SmartPLS software version 4.0.9.9. The data testing stages include validity testing, reliability testing, significance testing, and hypothesis testing. The results showed that public open space management had a positive relationship with visitor comfort in tourist areas, and environmental quality had a positive relationship with visitor comfort in tourist areas. Project management has a positive relationship with visitor comfort in tourist areas.
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 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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".