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Towards an AI-Driven Marketplace for Small Businesses During COVID-19
With the introduction of new COVID-19 variants such as Delta and Omicron, small businesses have been tasked with navigating a constantly changing business environment. Furthermore, due to supply chain issues, shortages of various critical products negatively affect businesses of all sizes and indust...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Nature Singapore
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371368/ https://www.ncbi.nlm.nih.gov/pubmed/35975091 http://dx.doi.org/10.1007/s42979-022-01349-w |
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author | Coltey, Erik Alonso, Daniela Vassigh, Shahin Chen, Shu-Ching |
author_facet | Coltey, Erik Alonso, Daniela Vassigh, Shahin Chen, Shu-Ching |
author_sort | Coltey, Erik |
collection | PubMed |
description | With the introduction of new COVID-19 variants such as Delta and Omicron, small businesses have been tasked with navigating a constantly changing business environment. Furthermore, due to supply chain issues, shortages of various critical products negatively affect businesses of all sizes and industries. However, continued innovation in Computer Science, specifically in sub-fields of Artificial Intelligence (AI), such as natural language processing (NLP), has created significant value for businesses through helpful data-driven features. To this end, we propose a platform utilizing AI-driven tools to help build an effective business-to-business (B2B) platform. The proposed platform aims to automate much of the market research which goes into selecting products and platform users during times of distress while still providing an intuitive e-commerce interface. There are three primary novel components to this platform. The first of these components is the Buyer’s Club (BC), which allows customers to pool resources to purchase bulk orders at a reduced cost. The second component is an automated system utilizing Natural Language Processing (NLP) to detect trending disaster news topics. Disaster topic detection can be applied to inform buyers and suppliers on adapting to changing market conditions and has been shown to match closely with Google Trends data. The third component is a regulation matching system, using a custom data set to help inform customers when purchasing products. Such guidance is necessary to comply with a regulatory environment that will be irregular for the foreseeable future. |
format | Online Article Text |
id | pubmed-9371368 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Nature Singapore |
record_format | MEDLINE/PubMed |
spelling | pubmed-93713682022-08-12 Towards an AI-Driven Marketplace for Small Businesses During COVID-19 Coltey, Erik Alonso, Daniela Vassigh, Shahin Chen, Shu-Ching SN Comput Sci Original Research With the introduction of new COVID-19 variants such as Delta and Omicron, small businesses have been tasked with navigating a constantly changing business environment. Furthermore, due to supply chain issues, shortages of various critical products negatively affect businesses of all sizes and industries. However, continued innovation in Computer Science, specifically in sub-fields of Artificial Intelligence (AI), such as natural language processing (NLP), has created significant value for businesses through helpful data-driven features. To this end, we propose a platform utilizing AI-driven tools to help build an effective business-to-business (B2B) platform. The proposed platform aims to automate much of the market research which goes into selecting products and platform users during times of distress while still providing an intuitive e-commerce interface. There are three primary novel components to this platform. The first of these components is the Buyer’s Club (BC), which allows customers to pool resources to purchase bulk orders at a reduced cost. The second component is an automated system utilizing Natural Language Processing (NLP) to detect trending disaster news topics. Disaster topic detection can be applied to inform buyers and suppliers on adapting to changing market conditions and has been shown to match closely with Google Trends data. The third component is a regulation matching system, using a custom data set to help inform customers when purchasing products. Such guidance is necessary to comply with a regulatory environment that will be irregular for the foreseeable future. Springer Nature Singapore 2022-08-11 2022 /pmc/articles/PMC9371368/ /pubmed/35975091 http://dx.doi.org/10.1007/s42979-022-01349-w Text en © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Research Coltey, Erik Alonso, Daniela Vassigh, Shahin Chen, Shu-Ching Towards an AI-Driven Marketplace for Small Businesses During COVID-19 |
title | Towards an AI-Driven Marketplace for Small Businesses During COVID-19 |
title_full | Towards an AI-Driven Marketplace for Small Businesses During COVID-19 |
title_fullStr | Towards an AI-Driven Marketplace for Small Businesses During COVID-19 |
title_full_unstemmed | Towards an AI-Driven Marketplace for Small Businesses During COVID-19 |
title_short | Towards an AI-Driven Marketplace for Small Businesses During COVID-19 |
title_sort | towards an ai-driven marketplace for small businesses during covid-19 |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371368/ https://www.ncbi.nlm.nih.gov/pubmed/35975091 http://dx.doi.org/10.1007/s42979-022-01349-w |
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