Web Strategies to Promote Internet Shopping: Is Cultural-Customization NeedeD?1
Bibliographic record
Abstract
Building consumer trust is important for new or unknown Internet businesses seeking to extend their customer reach globally. This study explores the question: Should website designers take into account the cultural characteristics of prospective customers to increase trust, given that different trust-building web strategies have different cost implications? In this study, we focused on two theoretically grounded practical web strategies of customer endorsement, which evokes unit grouping, and portal affiliation, which evokes reputation categorization, and compared them across two research sites: Australia (individualistic culture) and Hong Kong (collectivistic culture). The results of the laboratory experiment we conducted, on the website of an online bookstore, revealed that the impact of peer customer endorsements on trust perceptions was stronger for subjects in Hong Kong than Australia and that portal (Yahoo) affiliation was effective only in the Australian site. A follow-up study was conducted as a conceptual replication, and provided additional insights on the effects of customer endorsement versus firm affiliation on trust-building. Together, these findings highlight the need to consider cultural differences when identifying the mix of web strategies to employ in Internet store websites.
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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 | 0.003 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".