An examination of the status and evolution of country image research
Bibliographic record
Abstract
Purpose Country image (CI) has been one of the most studied topics in international business, marketing, and consumer behaviour of the past five decades. Nevertheless, there has been no critical assessment of this field of research. The purpose of this paper is to understand the status and evolution of CI research. Design/methodology/approach The authors review 554 articles published in academic journals over 35 years. The authors examine publication, authorship, and research procedure trends in these articles as an empirical and quantitative assessment of the field. The authors identify weaknesses and strengths, and the authors address disconcerting and encouraging trends. Findings The authors find a number of laudatory trends: CI research is becoming less US-centric, more theory driven, more sophisticated in methodology, evaluating more diverse product categories, and making use of multiple cue studies. There are, however, two major methodological concerns: poor replication and questionable generalizability of findings. The authors also noted the influence of CI articles has been decreasing, as well as their rate of publication in top tier journals. Originality/value Since the authors present data that reflect actual practices in the field and how such practices have changed across time, the authors believe the study is of substantial value to CI researchers, journal editors, and instructors whose curriculum includes CI. The critical assessment and subsequent recommendations are accordingly empirically justified.
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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 | 0.067 | 0.197 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".