The asymmetric influence of cognitive and affective country image on rational and experiential purchases
Bibliographic record
Abstract
Purpose – The purpose of this research is to differentiate and examine how country image (cognitive and affective image) has different impacts on product judgment and purchase intention in rational versus experiential purchases. Design/methodology/approach – A large-scale survey involving over 1,200 consumers was conducted in China. Structural equation modeling was used to analyze the data and test hypotheses. Findings – Empirical results show that the impact of country image on consumer purchase intention is mediated by general and category product image. In particular, the impact of cognitive country image on category product image is fully mediated by general product image in both rational and experiential purchases, whereas the affective country image has a direct impact on category product image in experiential but not in rational purchases. Research limitations/implications – This research extends the extant country-of-origin literature and shows that the product image dimension of the country-of-origin construct mediates the effect of the country image dimension of the country-of-origin construct on consumer purchase intention, and demonstrates the different effect of affective country image on product image in rational versus experiential purchases. Practical implications – The findings of this research can help multinational marketers, exporters and retailers to better decide when to benefit from their positive country image and avoid the potential pitfalls associated with negative country image. Originality/value – This study differentiates between cognitive and affective country image and between general and category product image. Thus, it provides insight to further understand how country image can influence consumer product judgment and purchase intention differently in rational and experiential purchases.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".