The sustainability of multinational enterprises' pandemic‐induced social innovation approaches
Bibliographic record
Abstract
Abstract The COVID‐19 pandemic has prompted an unprecedented reaction in several multinational enterprises (MNEs). These MNEs have adopted social innovation approaches to meet the needs of vulnerable societal groups by swiftly innovating their business models; drastically changing their product offerings and customer bases; and producing COVID‐19 necessities. These approaches have alleviated some key pandemic‐induced social challenges related to health and sanitation. In this perspective article, we use secondary sources of information to present and exemplify the various types of MNE pandemic‐induced social innovation approaches. We open the discussion on whether these approaches are transitory in nature or whether they can and should be sustained in the long‐term, given the right incentives to these MNEs. We conclude by redefining MNEs' social innovation and by suggesting avenues for scholars, practitioners, policymakers, and educators to support this momentum in MNEs which we argue, if sustainable, can be fruitful for addressing other pressing grand challenges such as climate change, food security, poverty, and inequality.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".