The effects of infrastructure and policy on e-business in Latin America and Sub-Saharan Africa
Bibliographic record
Abstract
This study investigates experts' assessments of the pertinent factors affecting e-business in developing countries from a theory-based national infrastructure perspective. We surveyed experts (business people, academicians, and officials of governmental and non-governmental organizations) in e-business in Latin America (LA) and Sub-Saharan Africa (SSA). Our partial least squares analysis shows that experts believed that policies targeted specifically toward e-business are important in affecting e-business capabilities and in obtaining value from e-business, more so than non-specific general information and communication technologies (ICT) policies, which are not significantly influential. ICT infrastructure generally affects e-business capabilities, though this was not found to be the case in Brazil. Experts believed that national government institutions positively affect e-business value in SSA, but not in LA. Experts did not believe that commercial infrastructure significantly affects e-business value. This study theoretically and empirically distinguishes between two different dimensions of e-business outcomes: specific capabilities and value derived from e-business. It operationalizes the effects of national government institutions and commercial infrastructure on e-business outcomes and empirically tests for their effects. The study provides empirical support for conceptual arguments for the need of ICT policies specific to the needs of e-business.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".