Bribery Practices of Three MNCs in the Host Countries: An Examination of the Issue from HRM Perspective
Bibliographic record
Abstract
This chapter examines three bribery cases of three different Multinational Corporations in three different countries from human resource management (HRM) perspective. The first case is about the bribery of Halliburton Energy Services of the USA in Nigeria, the second one is about the bribery of Scancem International ANS of Norway in Ghana and the third one is about the bribery of Niko Resources Ltd. of Canada in Bangladesh. The MNCs were involved in the bribery through their subsidiaries in the host countries. The top management of the MNCs and subsidiaries were involved in the bribery case. The cases show that the bribes were paid for protecting the interests of the subsidiaries in the host countries. The bribes were paid through some intermediaries and disguised in some ways. In examining the cases from HRM perspective, I looked at the relevant facts about the country and its government, the relevant facts about the MNCs and involved personnel(s), the relevant information about the case, the enablers of unethical behaviour and the HR lacunae in the case.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".