Bibliographic record
Abstract
We study the relationship between host market corruption pervasiveness, the subsidiary localization strategies implemented by MNEs and the likelihood of host market exit. We assume that the pervasiveness of corruption in the host market threatens to undermine the legitimacy of foreign-investing firms in the host market environment. In this context, the strategic insights proffered by resource dependence theory (RDT) and institutional theory (IT) are characterized by distinct spatial orientations. RDT predicts that subsidiaries will implement proximal (or, host market-oriented) localization strategies in which host country partners and employees are hypothesized to be best-suited to efforts to enhance the subsidiary’s legitimacy and reduce the likelihood of host market exit. Conversely, IT suggests that distal (or, home market-oriented) localization strategies, in which subsidiaries prefer to engage home country partners and employees in the subsidiary investment, are better-suited to reducing the likelihood of exit from increasingly corrupt host market environments. Leveraging this theoretical tension, we investigate the relative efficacy of these strategies by developing competing hypotheses with respect to the moderating impact of proximal and distal localization strategies upon the likelihood of market exit in increasingly corrupt host market environments. Testing the hypotheses with a sample of 1,239 subsidiary investments in 31 countries during 1998-2005, we find that a proximally-oriented partnering strategy heightens the likelihood of market exit under conditions of more pervasive host market public corruption, but not more pervasive private corruption. Conversely, a distally-oriented expatriate staffing strategy increases the likelihood of market exit under conditions of both more pervasive public corruption and private corruption.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".