Multinational enterprises’ approach to social innovation: key findings and future research avenues based on the systematic literature review
Bibliographic record
Abstract
Purpose Social innovation (SI) is seen as a cornerstone for addressing the major social and environmental challenges of today’s world.Given that multinational enterprises (MNEs) play a crucial role in contributing to a more sustainable world, this leads us to wonder about the potential of these innovation initiatives in the context of these firms. This systematic literature review aims to explore SI within these firms and suggest future research avenues, as well as highlight the implications of the subject. Design/methodology/approach Based on the analysis of 46 articles, this paper employs the PRISMA method to conduct a systematic literature review on SI within MNEs. Findings Drawing from the analysis of the results, this paper observes that SI within MNEs is generally mobilized within the framework of other responsible conceptualizations such as Corporate Social Responsibility (CSR); however, it remains a crucial lever for value creation in MNEs. Additionally, this review asserts that social innovation within MNEs acts as a catalyst for sustainability, social change, institutional effectiveness and knowledge sharing within these firms. Moreover, it illustrates the conditions for the success of this innovation in MNEs, including addressing the instrumental needs of target users, committing to the long term, the ability to shape the environment, maintaining a strong position among stakeholders and adapting new technologies. Originality/value This review offers a nuanced exploration of how SI manifests within MNEs, examining its diverse conceptualizations, functions and the conditions necessary for success. Building on this analysis, it highlights key theoretical, practical and policy implications, along with a series of research questions designed to establish a solid foundation for future 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 | 0.052 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.029 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".