Doing good for political gain: the instrumental use of the SDGs as nonmarket strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| 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 it