An Institutional Logics Approach to Liability of Foreignness: The Case of MNEs in Africa
Bibliographic record
Abstract
Prior research on firms’ liability of foreignness (LOF) has suggested that institutional differences cause and exacerbate LOF, and that overcoming LOF requires learning about the host country’s institutions and engaging in isomorphic behaviors. However, the institutional literature has not adequately considered how firms can overcome LOF in situations when isomorphic behaviors are not possible or desirable. In this paper, we use the emerging research on institutional logics and institutional entrepreneurship to address this key issue by examining case studies of eight foreign mining MNEs experiencing LOF in East Africa. Based upon our qualitative analysis, we find that conceptualizing LOF in terms of competing institutional logics can generate new insights, and that MNEs can overcome LOF by creating new institutional logics rather than conforming to existing ones. Even so, our data show that this is a difficult process, one that cannot be done unilaterally by the MNE – we find that local employees embedded in both sets of competing institutional logics can act as intermediaries who facilitate institutional entrepreneurship.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".