E-Government in Marketing a Country: A Strategy for Reducing Transaction Cost of Doing Business in Tanzania
Bibliographic record
Abstract
There are limited studies examining the role of Investment Promotion Agencies (IPA’s) and their respective marketing techniques used in attracting Foreign Direct Investment (FDI). Using an exploratory case study approach, this article addresses this research gap by exploring the role of e-government as a promotion technique in eliminating barriers to FDI inflows in Tanzania; particularly barriers related to information accessibility and bureaucratic procedures facing foreign investors in acquiring relevant licenses and business permits. The findings indicate that foreign investors utilize information to create knowledge of business environment in the host country, though some additional information may not be found due to informational specificity of a particular investment project. In addition, the findings indicate that implementation of e-government has reduced some monetary and non-monetary transaction costs of complying with government authorities. The article contributes to the existing body of knowledge in the field of marketing by examining the role of e-government services in the public sector marketing within a macro-marketing domain.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".