Unveiling the Power of Nation Branding: Exploring the Impact of Economic Factors on Global Image Perception
Bibliographic record
Abstract
Nation branding, which demonstrates countries’ power on an international platform, has gained prominence in the literature in recent years. How countries can build their strategies around these factors and make themselves attractive has become an issue of increasing interest to countries in recent years. Increasing a country’s role in the political arena, making the country more attractive to tourists, increasing the volume of foreign trade and foreign direct investment, and making the country more attractive in terms of skilled labor will improve its reputation and image, as perceived by other countries. The main objective of the study is to investigate the impact of foreign direct investment, tourism expenditure, human capital, and export on nation branding in the ten countries with the highest value in nation branding (USA, Germany, China, Japan, England, France, Italy, Canada, India, South Korea) applying the dynamic panel data model for the period 2010–2020. In the present study, we use the cross-sectional dependence, the slope homogeneity test, the CIPS unit root test, and the Generalized Method of Moments (GMM) method, one of the dynamic panel data methods. This study examined the factors involved in nation branding and found a positive and statistically significant relationship between exports, foreign direct investment, tourism, human capital, and nation branding.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".