Perceived Risk in Tuanzhu Group Buying and Traditional Online Buying in Taiwan
Bibliographic record
Abstract
Consumers who have adopted tuanzhu group buying (TGB) changed from one-on-one online buying to buying led by a tuanzhu (ie. group leader). The tuanzhu integrates the demands and funds of multiple consumers and is responsible negotiating with the seller to achieve better transaction results as well as to acquire more information about the product to reduce various kinds of risk. However, the TGB mechanism is marred by delays and uncertainties, which can trigger potential customer risks. We recruited as participants 193 college students from four business schools. All students participated in the research voluntarily. The respondents were randomly assigned to the traditional online buying and TGB groups. Of the 193 surveys distributed, 146 were returned. Results of this study show that consumers’ perceived risk is higher with TGB than with online buying. As a result, TGB is much more complicated than traditional online buying, and thus consumers’ perceived risk is higher with TGB than with traditional online buying.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".