Artificial Intelligence and Reduced SMEs' Business Risks. A Dynamic Capabilities Analysis During the COVID-19 Pandemic

Nick Drydakis

No 1045, GLO Discussion Paper Series from Global Labor Organization (GLO)

Abstract: The study utilises the International Labor Organization's SMEs COVID-19 pandemic business risks scale to determine whether Artificial Intelligence (AI) applications are associated with reduced business risks for SMEs. A new 10-item scale was developed to capture the use of AI applications in core services such as marketing and sales, pricing and cash flow. Data were collected from 317 SMEs between April and June 2020, with follow-up data gathered between October and December 2020 in London, England. AI applications to target consumers online, offer cash flow forecasting and facilitate HR activities are associated with reduced business risks caused by the COVID-19 pandemic for both small and medium enterprises. The study indicates that AI enables SMEs to boost their dynamic capabilities by leveraging technology to meet new types of demand, move at speed to pivot business operations, boost efficiency and thus, reduce their business risks.

Keywords: SMEs; Business Risks; COVID-19 pandemic; Artificial Intelligence; Dynamic Capabilities (search for similar items in EconPapers)
JEL-codes: L26 O33 Q55 (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-big, nep-ent and nep-sbm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/249568/1/GLO-DP-1045.pdf (application/pdf)

Related works:
Journal Article: Artificial Intelligence and Reduced SMEs’ Business Risks. A Dynamic Capabilities Analysis During the COVID-19 Pandemic (2022)
Working Paper: Artificial Intelligence and Reduced SMEs' Business Risks. A Dynamic Capabilities Analysis during the COVID-19 Pandemic (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:glodps:1045

Access Statistics for this paper

More papers in GLO Discussion Paper Series from Global Labor Organization (GLO) Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().