Chinese and French Consumer Perceived Risk in Online Shopping: The Role of Uncertainty Avoidance
Bibliographic record
Abstract
The perceived risks associated with online shopping have a critical effect on consumer decision making. Cultural values provide a good theoretical basis for understanding perceived risk. With such an increasing online consumer spending in China and France and significant cultural differences, better understanding of online shopping risk as perceived by e-shoppers in these two countries becomes particularly relevant. However, the research in the Chinese and French context is limited. Given this reality, the purpose of this study is to investigate non-personal and personal perceived risk differences in Chinese and French online consumers and to provide an explanation in cross-cultural perspectives. Both the Chinese and French respondents perceive low levels of non-personal and personal risk regarding their online clothing purchases. But it is interesting to note that the Chinese respondents perceive higher non-personal risk and personal risk than the French respondents, which is contrary to the expected results. This might be explained by the change of Chinese culture.Keywords: Non-personal perceived risk, personal risk, online shopping, cross-cultural,uncertainty avoidance, China, France.
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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 | 0.001 | 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) | 0.003 | 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".