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Artificial intelligence in business models as a tool for managing digital risks in international markets

Authors :
Buntić Luka
Damić Mate
Dužević Ines
Source :
SHS Web of Conferences, Vol 92, p 03005 (2021)
Publication Year :
2021
Publisher :
EDP Sciences, 2021.

Abstract

Research background: Through the ongoing trend of digitalization, organizations competing in international markets are getting more exposed to different technology related risks. Globalization and technology support enabled small tech-based companies to scale and expand their business. On the other hand, this has also led to a significant rise of different types of threats. Companies engaged in the process of internalization are more exposed to digital risks than companies competing on the local market. In order to help their companies to manage digital risks, governments use relevant institutions and resources. However, many organizations still largely depend on their own capabilities. A growing number of organizations uses artificial intelligence in business models as a new type of response to digital risks. Artificial intelligence could be the missing link that will help connect organizational and government resources for successful management of digital risks. Purpose of the article: To shed more light on this understudied issue, we conducted a literature review on the use of artificial intelligence in business models as a tool for managing digital risks on the global market. Methods: Literature review. Findings & Value added: We analysed the key determinants of artificial intelligence, their use in business models, and the way it can help organizations manage digital risks. Literature review summarizes the most important research on the topic and proposes new avenues for future research.

Details

Language :
English, French
ISSN :
22612424
Volume :
92
Database :
Directory of Open Access Journals
Journal :
SHS Web of Conferences
Publication Type :
Academic Journal
Accession number :
edsdoj.279dc2992234c15801c955d45b59b0a
Document Type :
article
Full Text :
https://doi.org/10.1051/shsconf/20219203005