Impact of Economic Freedom Distance on India’s Inbound Cross-Border Acquisition Volume: Moderating Role of Economic Distance
Bibliographic record
Abstract
Cross-border acquisitions (CBA) are a form of foreign direct investments and have been dramatically increasing over the last three decades. India has been one of the top CBA destinations among emerging economies, making it interesting to explore the determinants. Even though the CBA research is voluminous, the role of economic freedom is understudied. In this background, by extending the knowledge of distance measures impacting cross-border acquisition (CBA) activities, we examine the impact of economic freedom distance on India’s inbound CBA volume and the moderating role of economic distance. We used a sample of 979 observations by collecting the CBA data from Thomson’s EIKON Mergers and Acquisitions database for our study period covering 1990 to 2020. We show that economic freedom distance negatively impacts India’s inbound CBA volume. Moreover, economic distance significantly moderated their effect. These results indicate that India should strengthen its economic freedom and grow steadily to attract more CBA volume inflow. These findings have important theoretical and practical implications for multinational firms and policymakers in making emerging economies like India an attractive destination for CBA activities.
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 | 0.001 | 0.005 |
| 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 | 0.001 |
| 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".