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#5122. Improving emergency response operations in maritime accidents using social media with big data analytics: a case study of the MV Wakashio disaster
| August 2026 | publication date |
| Proposal available till | 07-06-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Strategy and Management; Management of Technology and Innovation; Decision Sciences (all); |
| place 1 | place 2 | place 3 | place 4 |
| Free | Free | Free | Free |
| 2350 $ | 1200 $ | 1050 $ | 900 $ |
Contract №5122.1   | Contract №5122.2 | Contract №5122.3 | Contract №5122.4 |
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
This paper aims to explore how big data analytics (BDA) emerging technologies crossed with social media (SM). Twitter can be used to improve decision-making before and during maritime accidents. We propose a conceptual early warning system called community alert and communications system (ComACom) to prevent future accidents. The authors adopted a post-constructionist approach through the use of media richness and synchronicity theory, highlighting wider community voices drawn from social media (SM), particularly Twitter. The authors reconstituted a narrative of four escalating sub-events and illustrated how critical decisions taken in an organisational and institutional vacuum led to catastrophic consequences. Our study shows that SM enhanced with BDA, embedded within our ComACom model, can better achieve collective sense-making of emergency accidents. This study is limited to Twitter data and one case. Our conceptual model needs to be operationalised. ComACom will improve decision-making to minimise human errors in maritime accidents. Emergency response will be improved by including the voices of the wider community.
Keywords:
Big data analytics; Human error; Maritime accidents; Media richness theory; Media synchronicity theory; Social media
Contacts :
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
This paper aims to explore how big data analytics (BDA) emerging technologies crossed with social media (SM). Twitter can be used to improve decision-making before and during maritime accidents. We propose a conceptual early warning system called community alert and communications system (ComACom) to prevent future accidents. The authors adopted a post-constructionist approach through the use of media richness and synchronicity theory, highlighting wider community voices drawn from social media (SM), particularly Twitter. The authors reconstituted a narrative of four escalating sub-events and illustrated how critical decisions taken in an organisational and institutional vacuum led to catastrophic consequences. Our study shows that SM enhanced with BDA, embedded within our ComACom model, can better achieve collective sense-making of emergency accidents. This study is limited to Twitter data and one case. Our conceptual model needs to be operationalised. ComACom will improve decision-making to minimise human errors in maritime accidents. Emergency response will be improved by including the voices of the wider community.
Keywords:
Big data analytics; Human error; Maritime accidents; Media richness theory; Media synchronicity theory; Social media
Contacts :
