Reputational judgments of foreign MNEs’ societal impact in frontier markets: the role of compatible, crossed, and conflicting signals
Bibliographic record
Abstract
Abstract How do host country stakeholders evaluate foreign MNEs’ local impact? Although MNEs’ desire for a reputation for positive societal impact is well-established in the literature, much less is known about how to actually obtain one—especially in less developed frontier markets. In this inductive, qualitative study across seven countries in sub-Saharan Africa, we examine why host country stakeholders deem some foreign MNEs to have a better reputation for societal impact than others and how firms’ actions and attributes influence these stakeholder perceptions. Leveraging signaling theory, we identify three distinct types of signals (compatible, crossed, and conflicting) and three critical factors (benefit diffusion, empowerment, and hybrid solutions) that shape MNEs’ reputation for societal impact. We also shed light on the role of contextual factors at the country, industry, and community levels. In addition to these theoretical contributions, our study also yields practical implications for MNEs of including local stakeholders’ perspectives when crafting market and nonmarket strategies, fostering constructive communication between headquarters and subsidiaries as well as between expatriate and local actors, and finding ways of going beyond ‘fitting in’ to instead ‘stand out’ in order to gain a reputation for providing tangible and intangible forms of societal impact in frontier markets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".