Doing good for political gain: the instrumental use of the SDGs as nonmarket strategies
Bibliographic record
Abstract
Abstract The United Nations Sustainable Development Goals (SDGs) are changing the way multinational enterprises (MNEs) engage with host governments. The SDGs offer MNEs a unique opportunity to build political influence by assisting governments in attaining a host country’s social needs. However, international business scholars have largely remained silent on how MNEs strategize to repurpose ‘doing good’ into political influence. Based on a multiple case study of four Western European MNE subsidiaries in Indonesia, we uncover the strategies that MNEs use to turn their SDG initiatives into political access and influence. Our study reveals three nonmarket strategies – SDG-directed cross-sector partnership, SDG-directed conflict management, and SDG-directed constituency building. These actionable strategies help MNEs manage the tensions arising from misaligned government priorities, high levels of perceived corruption, and skepticism toward foreign firms. Our findings advance the literature on international nonmarket strategy by explaining how MNE subsidiaries resolve these tensions and convert SDG-directed investments into political access and influence without succumbing to locally institutionalized norms of corruption. Finally, our study suggests that emerging-market governments may benefit from rewarding MNEs for their investments that contribute to the SDGs, as long as they provide clear guidance and multi-stakeholder platforms that foster effective collaborations with MNEs.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".