Motherland or home state? Allegiance of the Indian diaspora and its effect on reverse FDI
Bibliographic record
Abstract
Purpose – The purpose of this paper is to explore the potential regional preferences of the diaspora and explain how such preferences affect their decision when engaging in reverse Foreign Direct Investment (FDI). Since diasporas often act as conduits for trade and investment, the author is interested in whether these regional preferences affect their choice of destination for FDI. Design/methodology/approach – The author developed and pre-tested a questionnaire that was administered in pen and paper as well as online. Totally, 158 professional, managers and entrepreneurs with Indian diasporic background in the USA and Canada participated in the study. Follow-up interviews were conducted with 25 participants. Findings – Participants indicated that they did not favor their region of origin over the entire country. However, most of the participants only invested in their region of origin. Research limitations/implications – Interviews were based on the original survey questionnaire and did not further probe other issues. The current study should be treated as exploratory in nature and the results should be used as a springboard for future research. Practical implications – It would seem that the region of origin was important in the decision to migrate and for reverse FDI, even though cognitively the participants did not recognize it to the same extent. This might point to a mediation effect, which should be investigated in future studies. This paper helps businesses and governments understand the extent to which sub-national regional ties explain the investment motivations of people investing back in their home countries. Social implications – Furthermore, the importance of regional ties in the decisions to both invest and migrate point to the importance of studying sub-national cultural and institutional issues rather than treating large multicultural countries such as India as a monolithic bloc. Originality/value – The author used network ties theories to investigate and explain the investment behavior of Indian diaspora. While other disciplines (e.g. geography, sociology and economics) might have studied similar phenomena, the author looked and expanded the knowledge from a management perspective.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".