Digital-Platform-Based Ecosystems: CSR Innovations during Crises
Bibliographic record
Abstract
Humanitarian crises caused by war, natural disasters, famine, or disease outbreaks are growing globally and are persistent human tragedies threatening human health, safety, and well-being. Digital-platform-based ecosystems’ corporate social responsibility (CSR) activities have become a vital tool to support humans during crises. However, little is known about the impact of the innovative CSR practices of digital-platform-based ecosystems during a crisis. Therefore, this study investigates this crucial question. Building on dynamic capabilities theory and using thematic analysis of 89 news articles and data from website sources and reports relating to Airbnb Inc.’s CSR innovation in the Afghan 2021 and the Russia–Ukraine 2022 humanitarian crises, we find that strategic digital-platform-based ecosystem-driven CSR interventions during crises can be helpful for society and for businesses. The results suggest Airbnb.org leveraged its resources and capabilities to provide innovative, quick, and timely responses to redefine refugee resettlement, promoting a platform to harness community partnerships, creating a robust collaboration model with international non-governmental organizations and non-governmental organizations, and initiating a novel financial inclusion strategy for refugees and displaced persons. This result also implies that CSR technological innovations during s crisis can be theoretically explained and have further significant implications for policymakers, companies, and societal stakeholders.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".