Introducing Investment Promotion: A Marketing Approach to Attracting Foreign Direct Investment
Bibliographic record
Abstract
This paper introduces the concept of investment promotion, a form of marketing used by national governments to attract foreign investments into their country. While it is not a new concept, it barely makes it to academic literature. The paper brings investment promotion to the forefront for the purpose of creating a research interest in this topic and also contributes to the development of literature. It attempts to integrate investment promotion activities into already established marketing models and frameworks and set a marketing research agenda on the topic. Since attracting foreign investment is a policy and country situation issue, this paper will support and serve as a reference for governments as they design foreign investment policies and improve the attractiveness of their country as an investment destination using marketing tools. The paper first discusses the influence of marketing on investment promotion and how investors should be viewed as consumers who have needs to be satisfied. Various frameworks and concepts like the consumer decision-making process, market segmentation and marketing communications mix are discussed to show how they can be applied in investment promotion. The application of marketing concepts and theories has been beneficial in the business world, and this paper argues that there are potential benefits for countries who decide to apply the same concept and theories to attract investors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".