Marketing high‐tech products in emerging markets: the differential impacts of country image and country‐of‐origin's image
Bibliographic record
Abstract
Purpose The pupose of this paper is to investigate consumers' behavior in emerging countries. In particular, it simultaneously assesses the effects of country image and country‐of‐origin's image on consumers' uncertainty, aspiration and purchasing intention of high‐tech products. Design/methodology/approach Based on a sample of 479 Chinese consumers, structural equation modeling was used to test the hypothesized relationships. Findings Results show that compared to country‐of‐origin, country's image is a more effective tool in reducing consumers' uncertainty and increasing their aspiration to purchase high technology products. Contrary to country's image, however, country‐of‐origin's image plays a considerable role in influencing the product image. Research limitations/implications The major role of a country‐of‐origin is to influence product image while that of country's image is to increase consumers' aspiration to acquire its product and diminish their uncertainty and hesitation about buying the product. In other words, the image of a product is much more prone to the effect of country‐of‐origin's image than country's image. Practical implications Marketers should understand that consumers in emerging countries are ambivalent when they consider the purchase of complex products. On the one hand, highlighting the country image can contribute in alleviating consumers' uncertainty and increasing their aspiration to purchase sophisticated and complex products. On the other hand, promoting the country‐of‐origin's image can prove an effective means to improve product image in emerging markets. Originality/value Most of the previous studies have focused on one of the two concepts (i.e. country's image or country‐of‐origin), interchangeably used both of them, and relatively ignored their simultaneous impact on consumer behavior. The present study has tried to address this shortfall through simultaneously studying their influences on product image and consumer purchase intention; and highlighting their differential impacts.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".