Examining the Impact of Destination Image on Tourist Satisfaction and Loyalty at Lake Toba, Indonesia
Bibliographic record
Abstract
This study aims to test the influence of destination image on tourist loyalty satisfaction in Lake Toba, Indonesia.This study uses an explanatory research approach.The population of this study is all Domestic Tourists visiting the Lake Toba Region.Determination of the number of samples using the formula and obtained as many as 96 respondents.Primary data collection is done by questionnaire (questionnaire).The data analysis method used is Partial Least Square (PLS) based Structural Equation Modelling (SEM).The study results show that cognitive, unique, and affective images positively and significantly affect on tourist satisfaction in tourist destinations in the Lake Toba Region.Cognitive Image, Unique Image, and Tourist Satisfaction positively and significantly affect Tourist Loyalty in Lake Toba Tourism Destinations.However, the Affective Image only significantly affects Tourist Loyalty in Lake Toba Tourism Destinations.Cognitive Image and Unique Image have a positive and significant effect on Tourist Loyalty Through Tourist Satisfaction at Tourist Destinations in Lake Toba Tourism Destinations, and Affective Image has no significant effect on Tourist Loyalty Through Tourist Satisfaction at Tourist Destinations in the Lake Toba Tourism Destinations.Lake Toba Area managers must carry out various relevant programs and maintain good relations with stakeholders in maintaining tourism destinations.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".