A Cultural Perspective on Web Site Localization
Bibliographic record
Abstract
For the last two decades, companies have been increasingly operating in many foreign markets. Serving local consumers effectively and thus achieving success in individual foreign markets require both a solid understanding of local values and a reflection of this understanding on business activities in foreign markets that will facilitate serving local markets. Today, as the size of global E-Commerce is expanding, a Web site is an important medium of multinational companies in communicating with stakeholders. In this chapter, we will discuss from a cultural perspective how to create and manage Web sites that will enable multinational companies to successfully localize in their target markets. The localization model, a useful and comprehensive tool in Web site localization, indicates that effective Web sites must adopt the specific cultural characteristics for the local market beyond the simple content and the product/service localization. However, an analysis of some international (local) Web sites of the largest multinationals shows that although some multinationals localize their international Web sites well, many others are weak in reflecting even some key localization features let alone full localization in their local Web sites. Managers are advised to utilize the Web sites localization model to increase the effectiveness of their international Web sites.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".