Private Sector Corruption, Public Sector Corruption and the Organizational Structure of Foreign Subsidiaries
Bibliographic record
Abstract
Abstract Corporate anti-corruption initiatives can make a substantial contribution towards curtailing corruption and advancing efforts to achieve the United Nations’ Sustainable Development Goals. However, researchers have observed that underdeveloped assumptions with respect to the conceptualization of corruption and how firms respond to corruption risk impeding the efficacy of anti-corruption programs. We investigate the relationship between the perceived level of corruption in foreign host countries and the organizational structure of subsidiary operations established by multinational corporations (MNCs). Foreign host market corruption is disaggregated into two components—private and public corruption. We employ an uncertainty-based perspective grounded in transaction cost theory to focus upon the distinct mechanisms through which private and public corruption can each be expected to impact a foreign subsidiary’s organizational structure [wholly-owned subsidiary (WOS) or a joint venture (JV) with a local partner]. We expect that each type of corruption fosters a different type of uncertainty (environmental or behavioral) which predominates in shaping the MNC’s choice of foreign subsidiary investment structure. Hypotheses are developed and tested with a sample of 187 entries into 19 foreign host markets. Each type of corruption was found to exert a distinct effect upon the organizational structure of foreign subsidiaries. More precisely, while heightened perceived levels of public corruption were found to motivate MNCs to invest through a JV with a local partner rather than a WOS, more pronounced private corruption precipitated the opposite outcome.
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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 | 0.002 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".