The Impact of Stakeholder Management on Corporate International Diversification
Bibliographic record
Abstract
Abstract This article explores the relationship between stakeholder management and international diversification. We differentiate between strengths and concerns in stakeholder management to demonstrate the differential effects of the two aspects of stakeholder management. Previous research on stakeholder theory often focuses on the importance of stakeholder relations, trying to build a business case of relational capital that results from strong stakeholder management. Such a relational approach, however, overlooks the process of managing stakeholders, a process that allows firms with strengths in stakeholder management to develop an important capability of managing tensions. In this study, we argue that this capability, an inherent part of stakeholder management, can be critical when firms face increased complexity during the process of international diversification. We therefore propose that strengths in stakeholder management are positively related to international diversification, while concerns in stakeholder management are negatively related to international diversification. Using panel data for 169 US multinational firms over a 10‐year period, we find support that strengths in stakeholder management facilitate international diversification. We discuss the contributions of our findings to stakeholder theory and international business research.
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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 | 0.008 | 0.025 |
| 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 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".