Big tech and natural hazards: disaster response patterns of Fortune Global 500 ICT MNCs in developing countries
Notice bibliographique
Résumé
Purpose Climate change is anticipated to lead to an increase in worldwide natural calamities, with a disproportionate impact on less-prepared developing nations that struggle to cope with these events. This study aims to evaluate the disaster response efforts of Fortune Global 500 multinational corporations (MNCs) from the information and communication technology (ICT) sector in developing countries from 2015 to 2019. Design/methodology/approach This study evaluates the disaster response efforts of Fortune Global 500 MNCs from the ICT sector in developing countries from 2015 to 2019, based on content analysis of 246 (n) sustainability reports using MAXQDA content analysis software. Findings Findings indicate a rise in ICT MNCs involvement, with response instances growing from 44 to 59, driven by cash donations (38%), in-kind contributions (38%) and partnerships, notably with the Red Cross (46%). These efforts underscore ICT’s critical role in enhancing disaster management efficiency and coordination. However, a significant geographical disparity emerges: the share of responses in developing countries declined from 77% to 25% of total efforts, whereas those in developed nations increased from 23% to 58%, with 40% concentrated in the USA and Japan between 2017 and 2019. Despite a 50% profit margin increase, resource allocation favors headquarters proximity over vulnerability. Research limitations/implications The study was carried over a period of five years to showcase a longer trend, studies including a longer time period would be beneficial. However, five years was sufficient to start understanding some of the trends, especially that 2017 was included, which was historically the most devastating and costly weather and climate disasters year. In addition, this research relied on the examination of sustainability reports, which are published by the MNCs themselves, and the validity of the information in these reports is variable. The self-reported data may understate efforts in developing countries or overstate corporate social responsibility (CSR) optics, risking bias as firms control disclosures. The MAXQDA content analysis, while robust, depends on keyword selection, potentially missing nuanced activities. These gaps suggest caution in generalizing findings, urging practitioners and researchers to scrutinize reporting transparency and data scope. Practical implications MNCs should develop clear and transparent strategies for disaster response that outline their commitment to both developed and developing countries. These strategies should be integrated into their broader CSR initiatives and consider the specific needs and vulnerabilities of different regions. Policymakers should encourage MNCs to engage in disaster response through incentives and partnerships. They should also work to create an enabling environment that facilitates the effective deployment of ICT for disaster management, particularly in developing countries. NGOs should proactively engage with ICT MNCs to leverage their resources and expertise. They should also explore ways to strengthen partnerships and collaboration to ensure a more coordinated and effective response to natural disasters. Originality/value This study calls for further research into these geographical disparities and the long-term impacts of corporate responses, urging a reevaluation of strategies to ensure balanced and sustained disaster support, particularly as global natural disasters are expected to increase due to climate change.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».