Examining the Relationships of Cognitive, Affective, and Conative Destination Image: A Research on Safranbolu, Turkey
Bibliographic record
Abstract
Destination image is formed by three distinctly different but hierarchically interrelated components called cognitive, affective, and conative (Gartner, 1993:193). In this context, the main purpose of this research is to confirm the relationships between the cognitive, affective, and conative components of destination image. It also aims to reveal the multidimensional nature of cognitive destination image and determine the dimensions that compose it. Data for the sample was collected from 446 tourists who visited Safranbolu, Turkey. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and hierarchical regression analysis were conducted to test the hypotheses. The results show that the cognitive destination image is a multidimensional construct. Also it is confirmed that destination image is a hierarchical structure within the cognitive, affective, and conative components. The assessment of both the cognitive and the affective components of destination image can be used as a predictor of tourists’ behavioral intentions toward destinations, such as intention to revisit, recommend, and spread positive word of mouth. Moreover, it is revealed that the affective component is influenced by the cognitive component and the affective component also mediates the relationship between the cognitive and conative components. These results provide some theoretical and managerial implications.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".