Global Trends in Foreign Direct Investment: Findings from Bibliometric Analysis for Policy Recommendations
Bibliographic record
Abstract
Introduction: Foreign direct investment (FDI) is a crucial aspect of Global Value Chains (GVCs) and is recognized as a key driver of global economic growth. However, there has been a notable decline in investment activity, with a 10% drop in FDI compared to 2022. Current geopolitical tensions may contribute to the decline in FDI, but a closer examination reveals that the reduction is widespread across all sectors and countries. Objectives: This study delves into the current trends surrounding FDI and identifies the key factors that countries prioritize in their efforts to attract such investments. It aims to investigate which nations and institutions are directing more attention to FDI, as well as the sectors that organizations should focus on to enhance their capacity to secure additional investment. Methods: The bibliometric analysis data was sourced from the Scopus database. A comprehensive review of 241 papers was conducted for this study through PRISMA method. Results: The findings reveal that North America, China, Canada, and India emerged as the most interconnected hubs, establishing a significant research center for foreign investment. The United States and Canada were noted as the most productive countries, underscoring their global prominence in this field. The theme of globalization is gaining traction, emphasizing the necessity for sustainability in investment practices. However, geopolitical tensions have been identified as a major factor contributing to the decline in FDI. Conclusions: Policymakers are encouraged to adopt strategies to incorporate emerging themes such as sustainability and technological advancement to regain lost market share. Collaborating with leading researchers from countries like Canada and Australia can aid in identifying and implementing best practices to foster a more conducive investment environment. Focus on manufacturing sectors and trade liberalization policies can consistently attract more FDI. Thus ensuring the economic stability of the country..
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.015 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.139 | 0.237 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".