International information seeking behaviour of South African exporters
Bibliographic record
Abstract
While there are many requirements for making successful decisions, the raw material is information. Nowhere is this more evident than in the international arena. As Czinkota (1991:86) wrote: 'Information is even more important in the international setting, where entirely new parameters and environments are encountered.' Unfortunately, in spite of the growing importance of international trade, little knowledge exists about how firms gather information outside of their home country. The recognition of this lack of research has lead to a special call for papers by International Marketing Review in 2001, looking at this issue. There are a plethora of studies that look at how domestic firms gather information (e.g. Prescott and Bhardwaj, 1995, looked at the type of information collected by American firms). However, with the exception of a handful of studies (e.g. Bodur and Cavusgal 1985; Calof 1997; Hart, Webb and Jones 1994; Souchon and Diamantopoulos 1999; Mohamad, Zafar and Honeycutt 2001) it is not known whether these findings apply in an international context. More important for this study, research to date has focused on developed countries (Canada, United States and the United Kingdom) and not on an emerging or an economy in transition, such as the South African one
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".